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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
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Multilevel routing for data transmission in internet of things Bhawna Ahlawat; Anil Sangwan
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2065-2077

Abstract

Internet of things (IoT) is the network in which sensor nodes sense information and transmit sensed information to sink. The small size and far deployment of SN results in causing problems related to higher energy utilization. Many techniques have been proposed in the last years to improve lifetime of the network. The already developed methods are the clustering techniques which are path optimization algorithms. The virtual grid-based dynamic routes adjustment (VGDRA) is the protocol which is already been proposed to increase network’s duration. The VGDRA protocol improve life span but doesn't solve the issue of energy hole which affect network performance. This work aims to improvise the VGDRA algorithm to solve the power hole problem. The utilization of cache motes is done in the network and the sink will move energy to cache nodes for the data collection. MATLAB is executed to simulate the suggested model, and amount of dead motes, active ones and amount of packets, whose transmission is done to base station (BS).
Cross-layer multipath routing approach and link quality indicator for QoS provisioning in mobile WMSN Bharati S. Pochal; Jayashree Agarkhed; Siddarama R. Patil
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1288-1294

Abstract

With the recent advancement in mobile adhoc networks (MANET’s) and technology, applicability, and integration of wireless multimedia sensor networks (WMSN) in MANET’s has led to creation of smart distributed system for high-speed mobile multimedia streaming and real time multimedia traffic transmissions. In this paper, we propose cross-layer multipath routing approach with link quality indicator (CLMRLQI) to compute stable link between two nodes. CLMRLQI discovers stable multipath routes by considering cross-layer routing metrics such as energy and bandwidth to support quality of service (QoS). The simulation scenarios are carried on network simulation tool and QoS parameters such as throughput, PDR, delay, overhead and energy consumption are analysed.
Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia Tegar Arifin Prasetyo; Joshua Pratama Silitonga; Matthew Alfredo; Risky Saputra Siahaan; Roberd Saragih; Dewi Handayani; Rudy Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1353-1366

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.
Interoperability of Botswana’s healthcare systems using semantic prescription ontologies Eunice Chinatu Okon; Tshiamo Sigwele; Galani Malatsi; Tshepiso Mokgetse; Hlomani Hlomani
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.pp1782-1792

Abstract

The developing country of Botswana’s health information system faces interoperability challenges mainly due to the lack of shared patient medical data and histories between private and public healthcare providers, which leads to increased medical errors, increased healthcare costs, and potentially fatal outcomes. This research proposes an intelligent electronic prescription ontology (IEPO) framework to share Botswana’s patient electronic health records (EHRs) between private and public healthcare systems for a standardized and semantically rich data exchange. IEPO was evaluated for interoperability using the recall metric for completeness to capture the degree of all relevant information for exchange and the precision metric for accuracy performance to gauge the degree of error minimization during interoperability. The harmonic means of precision and recall called the F1- score, offered the overall interoperability performance. IEPO outperformed related works by 75% in recall, 54% in precision, and 76% in F1-score, demonstrating improved interoperability performance. Furthermore, IEPO was evaluated for correctness and expressiveness through competency questions via queries, results confirming correct and expressive responses.
Predicting progression of Alzheimer’s disease using new survival analysis approach Nour Saad Zawawi; Heba Gamal Saber; Mohamed Hashem
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp603-611

Abstract

It is critical to determine the risk of Alzheimer’s disease (AD) in people with mild cognitive impairment (MCI) to begin treatment early. Its development is affected by many things, but how each effect and how the disease worsens is unclear. Nevertheless, an in-depth examination of these factors may provide a reasonable estimate of how long it will take for patients at various stages of the disease to develop Alzheimer’s. Alzheimer’s disease neuroimaging initiative (ADNI) database had 900 people with 63 features from magnetic resonance imaging (MRI), genetic, cognitive, demographic, and cerebrospinal fluid data. These characteristics are used to track AD progression. A hybrid approach for dynamic prediction in clinical survival analysis has been developed to track progression to AD. The method uses a random forest cox regression approach to figure out how long it will take for MCI to turn into AD. In order to evaluate the result concordance index is used. The concordance index measures the rank correlation between predicted risk scores and observed time points. The concordance index was statistically considerably higher in the suggested work than in previous approaches with a score of 95.3%, which is higher than others.
An efficient controller-based architecture for AES algorithm using FPGA Nadaf, Reshma; Bhairannawar, Satish S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp397-404

Abstract

The importance of crucial current technical advancements, particularly those centered on the cryptography process such as Cryptographic advanced encryption standard (AES) hardware architectures are gaining momentum with respect to improving the speed and area optimizations. In this paper, we have proposed a novel architecture to implement AES on a reconfigurable hardware i.e., field programmable gate arrays (FPGA). The controller in AES algorithm is responsible to generate the signals to perform operations to generate the 128 bits ciphertext. The proposed controller uses multiplexer and synchronous register-based approach to obtain area and speed efficient on the FPGA hardware. The entire architecture of AES with proposed controller is implemented on Virtex 5, Virtex 6, and Virtex 7series using XilinxISE 14.7 and tested for critical path delay, frequency, slices, efficiency and throughput. It is observed that all the parameters are improved compared to existing architectures achieving the throughput of 32.29, 40.01, and 43.01 Gbps respectively. The key benefit of this approach is the high level of parallelism it displays in a quick and efficient manner.
Detecting attacks on e-mail Yujia Fang; Gabriela Mogos
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.pp1576-1588

Abstract

E-mail has become a popular communication tool widely used by universities, enterprises and governments. Despite the convenience it brought to people, attacks on e-mail happen very frequently in the range of the world, causing large economic loss and occupying a mass of network bandwidth every year. The hazards from e-mail attacks underline the importance of detecting and resisting spam in an efficient and timely way. Using Python, we built Na¨ıve Bayes (NB) and support vector machine (SVM) filters for emails. The filtering performance of NB and SVM email filters applying different kernel functions was compared and evaluated based on several evaluation indices including accuracy, precision, and total cost ratio (TCR). Also, in order to optimize the filters, the influences of stop words removal, feature numbers and other parameters in the filtering algorithms were monitored.
FedLANE: a federated U-Net architecture for lane detection Santhiya Santhiya; Immanuel Johnraja Jebadurai; Getzi Jeba Leelipushpam Paulraj; Polisetti Pavan Venkata Vamsi; Madireddy Aravind Reddy; Praveen Poulraju
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1621-1629

Abstract

Lane detection is a crucial module for today’s autonomous driving cars. Detecting road lanes is a challenging task as it varies in color, texture, boundaries and markings. Traditional lane detection techniques detect the lane by applying a model trained with centralized data. As roads vary in urban and rural areas, a more localized and decentralized training technique is desired for accurate and personalized lane detection. Federated learning has recently proved to be a promising technology that trains and prunes the model using local data. Applying federated learning-based lane detection improves the accuracy of detection and also ensures the security and privacy of autonomous cars. This paper proposes FedLANE, a federated learning-based lane detection technique. U-Net, U-Net long short-term memory (LSTM) and AU-Net architectures were explored using a federated learning approach. Experimental analysis using TuSimple and CuLane dataset shows that the FedLANE based lane detection performs similar to that of the traditional deep learning lane detection models.
Design and implementation of duty cycle-based futuristic clustering technique in WSN Trupti Shripad Tagare; Rajashree Narendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp951-959

Abstract

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.
A secure framework for effective workload resource management Dharuman Salangai Nayagi; Hosaagrahara Savalegowda Mohan
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp472-481

Abstract

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.

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