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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 31, No 2: August 2023" : 65 Documents clear
Brain tumor detection in the Spark system Soumia Benkrama; Nour Elhouda Hemdani
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp755-762

Abstract

Machine learning (ML) and computer vision systems revolutionized the world, especially deep learning (DL) for convolutional neural networks, which has proven breakthroughs in brain tumor (BT) diagnosis. This study investigates a convolutional neural network (CNN) approach for image classification for BT detection using the EfficientNetB1 architecture with global average pooling (GAP) layers in a big data setting. A classification layer is done with a softMax layer. The system is created in the Apache Spark environment. Spark system is a unified and ultra-fast analysis engine for large-scale data processing. It is mainly dedicated to big data and deep learning (DL). Experiments are carried out using the brain magnetic resonance imaging (MRI) dataset containing 3,264 MRI scans to predict the performance of the model. The dataset is decomposed into two datasets. The model's performance was assessed and compared to existing models, it yielded a high precision, precision, and f1-score. In our work, we have achieved an accuracy of 97% and a performance of 98% on a dataset of 3,064 brain MRI images.
Cybersecurity in health sector: a systematic review of the literature Catherine Vanessa Peve Herrera; Jonathan Steve Mendoza Valcarcel; Mónica Díaz; Jose Luis Herrera Salazar; Laberiano Andrade-Arenas
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1099-1108

Abstract

Currently, health centers are being affected by various cyberattacks putting at risk the confidential information of their patients and the organization because they do not have a plan or tools to help them mitigate these cyberattacks, which is important to know what measures to take to protect the privacy of personal data. The present work was carried out under a systematic literature review, which aims to show the importance of cybersecurity in the health sector knowing which tools are the most used and efficient to prevent a cyberattack. A systematic review of 301 articles was carried out, 79 of which are aligned with the objective set, fulfilling the inclusion and exclusion criteria. The search for information was carried out in the Scopus and Dimensions databases. The analysis carried out has resulted in good information that was compiled for the development of this topic, being favorable thanks to the different research of different authors.
Privacy aware-based federated learning framework for data sharing protection of internet of things devices Yuris Mulya Saputra; Ganjar Alfian
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp979-985

Abstract

Federated learning (FL) has emerged as one of the most effective solutions to deal with the rapid utilization of internet of things (IoT) in big data markets. Through FL, local data at each IoT device can be trained locally without sharing the local data to the cloud server. However, this conventional FL may still suffer from privacy leakage when the local data are trained, and the trained model is shared to the cloud server to update the global prediction model. This paper proposes a FL framework with privacy awareness to protect data including the trained model for IoT devices. First, a data/model encryption method using fully homomorphic encryption is introduced, aiming at protecting the data/model privacy. Then, the FL framework for the IoT with the encryption method leveraging logistic regression approach is discussed. Experimental results using random datasets show that the proposed framework can obtain higher global model accuracy (up to 4.84%) and lower global model loss (up to 66.4%) compared with other baseline methods.
Footprint biometric authentication using SqueezeNet Sairul Izwan Safie; Rusmawarni Ramli
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp893-901

Abstract

Biometric authentication is a process of identity verification once an identity is claimed by an individual. It uses unique features on the human body. Footprints are a new biometric feature that has sparked interest among researchers, as this feature is universal, easy to extract and has not changed throughout time. The focus of researchers in this field is to improve the recognition rate. Various techniques have been developed for this purpose, but the accuracy percentage is at 98% with an equal error rate (EER) of 6.1%. This paper proposes the use of a new technique called SqueezeNet in classifying footprint images. SqueezeNet belongs to the convolutional neural network (CNN) family. In this study, 300 footprint images were used from 15 individuals. The 70% of these images were used to train the proposed SqueezeNet network, while the rest were used for testing. At the end of this simulation, SqueezeNet has achieved an accuracy of 98.67% with an EER of 2.1%.
An adaptive combination algorithm based on deep learning and genetic algorithm for anomalous events detection Zainab K. Abbas; Ayad A. Al-Ani
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp902-908

Abstract

One of the most widely used human behavior detection methods is anomaly detection, which this article covers. Ensuring a person's safety is a crucial task in every community today due to the ever-increasing actions that can be dangerous, from planned crime to harm from an accident. Classic closed-circuit television is insufficient since a person must always be awake and available to monitor the cameras, which is costly. Also, someone's attention tends to decrease after a certain period of time. Due to these reasons, a surveillance system that is automated and able to detect unusual activities in real-time and give sufferers prompt aid is necessary. It should be noted that the identification process must be completed swiftly and correctly. In this paper, we employ a model based on mixes the machine learning (ML) model, namely genetic algorithms with deep learning (DL). In this study's experimentation, the UCF-Crime dataset was employed. The detection accuracy on the testing sample dataset was equal to 89.90%, while the area under the curve (AUC) was equal to 94.58%. The developed models have demonstrated reliability and the ability to achieve the greatest accuracy when compared to models that have already been designed.
Blind nonlinear unmixing using nonnegative matrix factorization based bi-objective autoencoder Sreejam Muraleedhara Bhakthan; Agilandeeswari Loganathan; Aashish Bansal
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1070-1079

Abstract

Hyperspectral image processing is one of the trending techniques used in many fields such as remote sensing, medical, agriculture, food processing, and military. The unique discrimination of hyperspectral images can be used for object identification, classification, and prediction. One of the main challenges of these tasks is the mixed pixel problem. Hyperspectral unmixing is the process of identifying the endmembers and their abundance in pixels. In linear unmixing, the mixture of the endmembers is assumed to be linear homogenous patches. Even though these models are simple and faster in performance, most of the real-world images are not linear. A modified nonlinear mixture-based sparsity regularized bi-objective autoencoder model based on nonnegative matrix factorization (NMF-BOA) is proposed in this article. The performance analysis shows that our model gives competitive results compared to the state-of-the-art models.
Network intrusion detection and classification using machine learning predictions fusion Harshitha Somashekar; Ramesh Boraiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1147-1153

Abstract

The primary objective of an intrusion detection system (IDS) is to monitor the network performance and to look into any indications of malformation over the network. While providing high-security network IDS played a vital role for the past couple of years. IDS will fail to identify all types of attacks, when it comes to anomaly detection, it is often connected with a high false alarm rate with accuracy and the detection rate is very average. Recently, IDS utilize machine learning methods, because of the way that machine learning algorithms demonstrated to have the capacity of learning and adjusting as well as permitting a proper reaction for real-time data. This work proposes a prediction-level fusion model for intrusion detection and classification using machine learning techniques. This work also proposes retraining of model for unknown attacks to increase the effectiveness of classification in IDS. The experiments are carried out on the network security layer knowledge discovery in database (NSL-KDD) dataset using the Konstanz information miner (KNIME) analytics platform. The experimental results showed a classification accuracy of 90.03% for a simple model to 96.31% for fusion and re-trained models. This result inspires the researchers to use machine learning techniques with a fusion model to build IDS.
Bluetooth low energy for internet of things: review, challenges, and open issues Mahmood A. Al-Shareeda; Murtaja Ali Saare; Selvakumar Manickam; Shankar Karuppayah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1182-1189

Abstract

As a result of its ultra-low power consumption, simple development, sufficient network coverage, and rapid data transfer speed, Bluetooth low energy (BLE) has emerged as the standard communication standard for internet of things (IoT) nodes. Therefore, in this review paper introduces the concept of Bluetooth low energy for the internet of things (BLE-IoT) in terms of Bluetooth classic, Bluetooth version, applications for BLE-IoT, and new features of BLE-IoT. We then provide a taxonomy of literature reviews based on the parameter adjustment approach (e.g., advertiser side schemes, scanner side schemes, hybrid schemes) and collision avoidance approach (e.g., advertiser side schemes and scanner side schemes). Finally, we discuss research challenges and future opportunities for BLE-IoT.
Optimal placement of the phasor measurement units using differential evolution algorithm Mahmoud Zadehbagheri; Alireza Abbasi; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp1211-1222

Abstract

The increasing consumption of the electric energy aimed at develop the transmission networks and the demand for higher reliability from the network; in this regard, wide-area measurement systems using phasor measurement units (PMUs) have revolved the trend of power network management. In this paper, the optimal allocation of PMUs in order to reach the perfect observability of the network; based on a differential evolution algorithm, is proposed and it is shown that, the deployment of constraints related to the zero-injection busses (ZIB) aimed to decrease the number of PMUs and their corresponding cost. By comparing the proposed method to the other methods, its simplicity and good performance are approved.
Data mining technique for grouping products using clustering based on association Eka Praja Wiyata Mandala; Dewi Eka Putri
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp835-844

Abstract

There is high competition between these minimarkets so many products sold in each minimarket are not sold until they expire. The aim of this study is to help retail managers cluster products in minimarkets. The data obtained will be processed using the hybrid data mining approach by combining two methods in data mining. In the first section, association uses the FP-Growth algorithm, and in the second section, clustering uses the K-means algorithm. From the experimental results, it can be seen that the proposed approach can minimize the number of products to be grouped. After the association process is carried out, from 29 products in 12 transactions, 6 products can be obtained that has a frequency above the minimum support and minimum confidence. After the clustering process, 6 products are grouped into 2 clusters, so that 1 product is included in the most interested product cluster and 5 products are included in the interested product cluster. We minimize data processing so that retail managers can process data directly from sales transaction data on the cashier's computer and can quickly get the results of product grouping.

Page 4 of 7 | Total Record : 65

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