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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 64 Documents
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Search results for , issue "Vol 33, No 3: March 2024" : 64 Documents clear
Pothole detection in bituminous road using convolutional neural network with transfer learning Mukesh Kumar Tripathi; Donagapure Baswaraj; Shyam Deshmukh; Kapil Misal; Nilesh P. Bhosle; Sunil Mahadev Sangve
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1950-1957

Abstract

The challenges of road maintenance, particularly in detecting potholes and cracks, and the proposed method using transfer learning and convolutional neural networks (CNNs) are significant advancements in this domain. Transfer learning is particularly beneficial, as it allows leverage pre-trained models to enhance the performance of the pothole detection system. CNNs, with their ability to capture spatial hierarchies in data, are well-suited for image-based tasks like pothole detection. The potential applications of the suggested method for intelligent transportation systems (ITS) services, such as alerting drivers about real-time potholes, demonstrate we research’s practical implications. This contributes to road safety and aligns with the broader goals of innovative city initiatives and infrastructure management. Achieving a 96% accuracy rate is a significant result, indicating the robustness of the proposed approach. Using this information to assess initial maintenance needs in a road management system is forward-thinking. Overall, we work is a valuable contribution to intelligent transportation and infrastructure management, showcasing the potential of advanced machine-learning techniques for addressing critical issues in road maintenance.
Image classification-based transfer learning framework for image detection of IoT devices Yuris Mulya Saputra; Hanung Addi Chandra Utomo; Ganjar Alfian
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1989-1997

Abstract

For artificial intelligence (AI) applications, centralized learning on a cloud server and local learning on an internet-of-things device may suffer from data privacy leakage due to data sharing and inaccurate prediction due to limited computing resources. Transfer learning has been proposed as one potential solution to the world's big data problems. Transfer learning eliminates the need for each internet-of-things device to share local data with the cloud server during the training process. Instead, it can go through the training process on its own, using a cloud server's pre-trained model with high accuracy. As a result, despite its limited computing resources, the internet of things device can still predict with high accuracy. This paper proposes a transfer learning model for improving image detection accuracy on IoT devices with restricted computation. To obtain accurate image classification, a deep learning approach based on convolutional neural networks is used. The proposed method with freeze and unfreeze approaches achieves a higher validation accuracy (up to 43.6%) and a lower validation loss (up to 6.5 times) than the non-transfer learning method, according to simulation results using three relevant internet-of-things datasets.
Development of a wearable monitor to identify stress levels using internet of things Nurassyl Zholdas; Octavian Postolache; Madina Mansurova; Baurzhan Belgibaev; Murat Kunelbayev; Talshyn Sarsembayeva
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1486-1499

Abstract

Modern life's ubiquitous component of stress has a significant impact on many facets of human existence. This article presents the development of a wearable device integrated with internet of things (IoT) technology, aiming to identify and quantify stress levels in real-time. This technology provides a possible means of improving stress assessment, enabling prompt treatments and individualized stress management techniques. ESP32-PICO computation platform was used as part of wearable stress monitor. The developed wearable monitor also includes a high-sensitivity pulse oximeter and heart-rate sensor (MAX30102) and galvanic skin response (GSR) sensors to acquire physiological signals associated with stress status. The wearable monitor device delivers data to the firebase platform via Wi-Fi. The benefits and prospective uses of the IoT-enabled wearable device are also covered in the article. It demonstrates the mobile wearable monitor adaptability in a variety of scenarios, such as offices, classrooms, and healthcare facilities, where stress management is vita and required for activity optimization. Continuous monitoring capabilities allow users to learn about their stress levels and take proactive self-care measures. During the validation experiments, the accuracy of measurement capabilities of the developed wearable monitor were evaluated reduced errors of heart rate and respiratory rate being observed.
Authentication schemes in wireless internet of things sensor networks: a survey and comparison Pendukeni Phalaagae; Adamu Murtala Zungeru; Boyce Sigweni; Selvaraj Rajalakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1876-1888

Abstract

The proliferation of wireless sensor networks (WSNs) fuels internet of things (IoT's) rapid global development, connecting diverse devices. IoT transforms devices into intelligent entities delivering exceptional services. This work addresses IoT authentication gaps through a comprehensive survey, analyzing recent works and exploring techniques in various applications. It includes a comparative analysis of authentication schemes, evaluating Bi-Phase authentication scheme (BAS) in WSNs. BAS outperforms sensor protocol for information via negotiation (SPIN), broadcast session key protocol (BROSK), and localized encryption and authentication protocol (LEAP), resulting in lower energy consumption and higher efficiency. With energy efficiency at 60 Kb/J for 25 nodes, BAS focuses on power optimization and lightweight security measures, reducing energy consumption, maximizing efficiency, and extending WSN lifespan. The evaluation, conducted using MATLAB/Simulink, demonstrates BAS's superiority, achieving 10 J, 12 J, 14 J, and 15 J energy consumption for 25 nodes during simulation, showcasing its effectiveness and future potential in advancing IoT authentication.
Predicting likelihood of fraud among financial distressed firms in Malaysia using textual analysis Marziana Madah Marzuki; Syerina Azlin Md Nasir; Siti Fadilah Mat Zain; Nik Siti Madihah Nik Mangsor
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1620-1631

Abstract

This research paper aims to analyze and predict fraud patterns among failed companies in Malaysia. The approach involves utilizing textual analysis on the management discussion and analysis (MD&A) section within the annual reports. The dataset is subjected to text clustering to group companies based on similar financial characteristics. This clustering process entails several steps, including data conversion, collation, and summarization into a structured format, followed by text pre-processing to cleanse the dataset. Notably, RapidMiner Studio software was utilized to extract data for the study. Subsequently, the documents are clustered using both the K-means and latent dirichlet allocation (LDA) methods. Upon examining a sample of 22 failed companies in the year 2020, the study reveals that financially distressed companies exhibit prominent financial negativity and utilize litigious financial terms within their MD&A sections. These linguistic traits are found to be closely associated with seven distinct characteristics of fraudulent firms. This preliminary findings provide compelling evidence that financial pressure may serve as a triggering factor for fraudulent activities within companies.
Arnold’s cat map secure multiple-layer reversible watermarking Aulia Arham; Novia Lestari
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1536-1545

Abstract

Reversible watermarking is a novel approach to digital copyright protection that allows the embedding of watermarks into digital data using multiple layers while retaining the ability to recover the original content without data loss. This method provides a unique solution for securing digital data while maintaining the integrity and quality of the content. Nonetheless, new challenges have emerged with the increase in attacks on this method, as reversible watermarking methods lack security keys, making it easy to extract and modify hidden data. In this paper, we present a method for multiple-layer reversible watermarking with security keys, with the goal of addressing the challenges posed by attacks and improving data protection within embedded content. The method uses arnold’s cat map to scramble images, and data embedding in predetermined iterations serves as the methods security key. We put the method through its paces with six grayscale images. With this method, the embedding capacity can reach 2.999 bpp across four layers of embedding, while the visual image quality can reach 22.01 dB. The outcomes from this approach are that the security of multiple-layer reversible watermarking can be enhanced while preserving the capacity to embed data in each layer.
MAPATON 2023: implementing a tool for the analysis of forest fire zones in Arequipa Natalia I. Vargas-Cuentas; Meyluz Paico-Campos; Abel Cahuana; Elber E. Canto-Vivanco; Tania Valencia; Sebastian Ramos-Cosi; Peter Villena; Avid Roman-Gonzalez
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1511-1523

Abstract

The "Mapaton 2023," a collaborative initiative by the Peruvian Space Agency, Paraguayan Space Agency, and AMERIGEOS, introduces an innovative approach to analyze fire-prone areas in Arequipa, a pivotal endeavor for disaster prevention. Focused on the provinces of Arequipa and Caylloma, recent studies identified over 1,500 affected hectares by forest fires, emphasizing the urgency of employing satellite images and geospatial analysis techniques. Leveraging Landsat 8 satellite imagery, the research calculated indices, including the normalized difference vegetation index (NDVI) for vegetation analysis and the differenced normalized burn ratio (dNBR) for fire severity assessment. Results revealed varying impacts, with some areas exhibiting increased vegetation and others displaying significant damage. The use of ArcGIS online facilitated the presentation of geospatial data, emphasizing the utility of remote sensing in comprehending and addressing forest fires. Drawing insights from analogous studies in Mexico and the Amazon, this research underscores the importance of remote sensing and geospatial analysis in informing preventive measures against wildfires. The findings, crucial for environmental management, are recommended for sharing with relevant authorities, and the continued use of diverse satellite imagery sources is encouraged to enhance accuracy in monitoring and mitigating forest fires.
Evaluation of filtering and contrast in X-ray and computerized tomography scan lung classification Anitha Nagaraja Setty; Rajesh Thalwagal Mathad; Krishnatejaswi Shenthar; Likhith Likhith
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1715-1725

Abstract

Deep learning provides many convenient methods to help medical practitioners take informed decisions about diverse ailments. The goal of this project is to measure the effectiveness of filters and contrast enhancement techniques qualitatively and quantitatively in classifying lung scan images. Transfer deep learning was used to obtain the necessary results, with DenseNet 121 being the base model. Salt and pepper filter was used to introduce noise, and 3×3 mean and 5×5 mean with contrast limited adaptive histogram equalization (CLAHE) was used to minimize the effect of noise. All layers excluding the rearmost were frozen, and new dense and dropout layers were added to identify features of computerized tomography (CT) scan images of lungs. The resultant models were of comparable accuracy, where the model with no filter gave the accurate results for the given data, and the one using the 5×5 mean filter gave better adaptability in classification of unseen data. The misclassification between normal and pneumonia affected lungs is relatively higher, because of the lack of distinct features between them.
Analysis and design on acceptance of blockchain based e-voting system Ambuj Shukla; Debani Prasad Mishra; Anwesh Pattnaik; Surender Reddy Salkuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1793-1801

Abstract

Elections are a critical aspect of democratic governance, providing citizens with the power and right to express their views. A secure voting system with innovative features can improve this process. Blockchain technology is considered a disruptive innovation, and its potential for enhancing the e-voting system is significant. The modern voting system is focusing more on blockchain technology to strengthen and secure the process. Blockchain is a reliable, decentralized database that can offer increased security compared to electronic voting machines (EVMs). This research paper presents a detailed study of the design, smart contracts, evaluation of action, and survey on the acceptance of blockchain-based e-voting systems. It examines the requirements for such a system and provides an understanding of the model. As the acceptance of information technology-based services and products increases, future innovation in the e-voting system may depend on blockchain technology. The survey conducted in this paper explores the differences in opinion based on gender, age, and profession among eligible voters from India regarding the acceptance of blockchain technology-based secure e-voting systems. The analysis of these differences sheds light on the potential for blockchain-based e-voting systems to enhance trust and security in the voting process.
Design a self-controlled high-performance evaluation of content addressable memories using 45 nm technology Saidulu Inamanamelluri; Devaraj Dhanasekaran; Radhika Baskar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1397-1404

Abstract

A special type of random access memory (RAM) array called as content addressable memory (CAM), in which stored data is compared with the search data which can be returning the address. In the applications of highspeed searching, the CAMs are used. NOR type matchline CAMs are helpful for applications requiring faster search speeds. Because the NOR type match line (ML) CAM consumes a lot of power, therefore many published designs have attempted to reduce power consumption. The design self-controlled high-performance content addressable memories (SC-CAM) using 45-nm technology is presented in this paper. The 6T 4×4 CAM arrays in this paper uses SC logic and Tanner tools 45-nm technology. When compared to the conventional CAM, described SC-CAM architecture reduces the number of voltages sources. Described 6T 4×4 SC-CAM design needs less number of MOSFETs than existed 8T 4×4 CAM array and thus reduces the area with high speed.

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