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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 30, No 2: May 2023" : 64 Documents clear
Two-level frame aggregation with enhanced A-MPDU for signal-to-noise ratio efficiency in IEEE 802.11n WLANs Fatoumata Sorra; Mohamed Othman; Umar Ali Bukar; Fahrul Hakim; Mohamed A. Alrashah; Anvar Saif; Mehrnaz Moudi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1038-1046

Abstract

One of the most important frame aggregation features for enhancing the speed of IEEE 802.11n wireless local area networks (WLANs) through sharing headers and timing overheads is an aggregate MAC protocol data unit (A-MPDU). However, because aggregation overhead affects A-MPDU frame size, the A-MPDU performance falls short of user expectations. The variable signal-to-noise ratio (SNR) is significantly decreased as a result of the influence of lost sub-frame on the volume of sub-frames that may be aggregated (the level of aggregation). In order to solve this issue, this study suggests an improved A-MPDU with reduced header overheads as well as a efficient two-level aggregation technique based on enhanced A-MPDU. To test the suggested plan, a simulation experiment was run on NS-3. The results show that the suggested two-level aggregation approach works better than the existing methods by achieving higher throughput, SNR, and a efficient medium access control (MAC) layer.
A remote health monitoring framework for heart disease and diabetes prediction using advanced artificial intelligence model Adari Ramesh; Ceeke Kalappagowda Subbaraya; Ravi Kumar Guralamata Krishnegowda
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp846-859

Abstract

Remote health monitoring frameworks gained significant attention due to their real intervention and treatment standards. The proposed work intends to design an artificial intelligence (AI) based remote health monitoring framework for predicting heart disease and diabetes from the given medical datasets. In this framework, the smart devices are used to gather the health information of patients, and the obtained information is integrated together by using different nodes that includes the detecting node, visualization node, and prognostic node. Then, at that point, the health care dataset preprocessing is performed to standardize the characteristics by recognizing the missing qualities and taking out the unessential characteristics. Consequently, the unified levy modeled crow search optimization (ULMCSO) algorithm is employed to select the optimal features based on the global fitness function, which helps increase the accuracy and reduce the training time of the classifier. Finally, the probabilistic guided naïve distribution (PGND) based classification model is utilized for predicting the label as to whether normal or disease affected. During an evaluation, two different datasets, such as PIMA and Hungarian, are used to validate and compare the results of the proposed model by using various performance measures.
Cryptocurrencies investment framework using sentiment analysis of Twitter influencers Mohammed Ali Zare Chahooki; Kia Jahanbin; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1068-1079

Abstract

In recent years, cryptocurrency technology has become an attractive area for investment due to its transparency, independence, and non-transactional nature. Many analysts and researchers talk daily on social media about the future of various cryptocurrencies. These ideas can significantly impact whether or not people are willing to invest. This paper provides a framework to help traders learn about the opinions of influential people and organizations in the field. Over the course of six months, the sentiment of more than 90 significant Twitter users was extracted for the proposed framework. In this study, we used the Vader open-source tool for sentiment analysis. This paper provides an excellent opportunity for investment through sentiment analysis of lesser-known or emerging cryptocurrencies. Also in this paper, we introduce the user importance factor to calculate the value of each tweet based on the number of retweets and comments. This factor shows the importance of their opinions instead of considering the number of followers of the authors. This factor causes a lower coefficient to be assigned to an author's opinion if it decreases in importance over time. The results show that in the short and long term, users' opinions are very effective in the market for cryptocurrencies and in predicting its price  trend.
Deep learning for classifying thai deceptive messages Panida Songram; Suchart Khummanee; Phatthanaphong Chomphuwiset; Chatklaw Jareanpon; Laor Boongasame; Khanabhorn Kawattikul
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1232-1241

Abstract

Online deception has become a major problem affecting people, society, the economy, and national security. It is mostly done by spreading deceptive messages because message are quickly spread on social networks and are easily accessed by anyone. Detecting deceptive messages is challenging as the messages are unstructured, informal, and complex; this extends into Thai language messages. In this paper, various deep learning models are proposed to detect deceptive messages under two feature extraction trials. A balanced two-class dataset of deceptive and truthful Thai messages (n=2378) is collected from Facebook pages. Instance features are encoded using word embeddings (Thai2Fit) and one-hot encoding techniques. Five classification models, convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), CNN-BiLSTM, and CNN-BiGRU, are proposed and evaluated upon the dataset with each feature extraction technique. The experimental results show that all the proposed models had excellent accuracy (95.59% to 98.74%) and BiLSTM with one-hot encoding gave the best performance, achieving 98.74% accuracy.
Resource allocation optimization for mitigating multi-jammer in underwater sensor network Sheetal Bagali; Ramakrishnan Sundaraguru
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp965-971

Abstract

Wireless underwater sensor networks (UWSNs) are used for coastal area monitoring and military monitoring applications, such as tsunami prevention and target tracking. In UWSNs, jamming is considered to be a serious problem where the intruder affects the lifetime of sensor motes and impacts the performance of the packet transmission. This paper considers that the jammer device is capable of reducing battery life and preventing the trustworthy UWSN mote from communication. Considering the presence of multiple jammers, the existing resource utilization model is not effective. This work presents an efficient resource allocation design to mitigate multiple jammers in UWSNs to overcome research problems. The resource allocation (ERA) model adopts a cross-layer design and can interact in a cooperative manner using direct and hop-based communication to maximize the quality of resource use. Compared to existing resource allocation methodologies, considering the presence of multiple jammer motes, the ERA achieves a much better detection rate, resource utilization, packet drop, and energy efficiency performance.
Intelligent malware classification based on network traffic and data augmentation techniques Ammar D. Jasim; Rawaa Ismael Farhan
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp903-908

Abstract

To prevent detection, attackers frequently design systems to rearrange and rewrite their malware automatically. The majority of machine learning techniques are not sufficiently resistant to such re-orderings because they develop a classifier based on a manually created feature vector. Deep learning techniques like convolutional neural networks (CNN) have lately proven to perform better than more traditional learning algorithms, especially in applications like picture categorization. As a result of this success, CNN network proposed with data augmentation techniques (to enhance the performance) to classify malware samples. We trained a CNN to classify the photos using converted grayscale images from malware files. Our methodology outperforms other methods with an accuracy of 98.80%, according to experimental results.
Peak to average power ratio reduction in spectrally efficient FDM using repeated clipping and filtering Buthaina Mosa Oman; Yamaan Esmaeel Majeed; Iftekhar Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp993-1001

Abstract

Multi-carrier transmission may be considered one of the important developments in wireless communications. Spectrally efficient frequency division multiplexing (SEFDM) is a promising multi-carrier modulation which can significantly improve utilization of spectral. The SEFDM has high peak to average power ratio (PAPR) like any multicarrier system. High PAPR reduces the random forest (RF) transmitter power amplifier efficiency, which minimize the use of this technique in limited power supply transmitters. In this work, a repeated clipping and filtering method is introduced to reduce the PAPR in SEFDM with minimum or no out of band radiation. The results of the simulated approach show that the PAPR of the SEFDM was reduced from 16.264 dB to 7.9146 dB with marginal degradation in system performance when the clipping ratio varied from 4 to 2.
A new motion structure for a six-legged insect robot with Bluetooth remote-control Mazin Abdulelah Alawan; Ali Kadhim Abdulabbas; Oday Jasim Mohammed Al-Furaiji
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp760-769

Abstract

This paper focuses on motion which is considered an important issue for insect robots, which consumes significant energy sources in general. We inspired the robot design structure from the challenges that crawling insects face in nature, especially a cockroach. The proposed robot configuration enhanced error scale such as energy modulation, computation, and mass. This paper presents the design and construction of six-legged robot with minimum number of trigger motors and the movement mechanisms used for the leg movements. The insect robot resembles a cockroach in shape and size (1.6 cm × 4 cm) and can move at rates of up to 3.5 cm per second. The robot can operate for up to 260 minutes. Additionally, it has a camera that can rotate more than 60 degrees in response to commands from a smartphone. The 160×120 pixels monochrome "first person" camera transmits video to a Bluetooth radio at a distance of up to 120 meters away at a frame rate of 1 to 5 per second.
Covariance absolute values spectrum sensing method based on two adaptive thresholds Bushra T. Hashim; Hadi T. Ziboon; Sinan M. Abdulsatar
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1029-1037

Abstract

Cognitive radio is a modern wireless communication methodology that deals with the issue of spectrum untapped. Cognitive users can continually perceive the licensed spectrum to hunt for unoccupied spots. The essential technology in cognitive radio (CR) for primary user detection is spectrum sensing. Due to signal to noise ratio (SNR), noise uncertainty in spectrum sensing would make the detection unreliable. In this paper, the two adaptive thresholds based on_covariance absolute values (TATCAV) are proposed to increase detection performance in the presence of noise uncertainty. According to the computer simulations using MATLAB 2021b, the value of the probability of detection is Pd=98.1% Compared with the results of two thresholds based on_covariance absolute values (TTCAV) Pd= 95.3% at SNR=-18, noise uncertainty Nu=1.761 dB, and using quadrature amplitude modulation (QAM). And the error rate for the proposed approach is Pe=12.1% under the same circumstances. The proposed approach results, according to the simulations, are considerably better than the results of the fixed two-threshold approach.
Hermitan matrices based malicious cognitive radio detection and bayesian method for detecting primary user emulation attack Devasahayam Joseph Jeyakumar; Boominathan Shanmathi; Parappurathu Bahulayan Smitha; Sekar Vinurajkumar; Mohanan Murali; Muthuraj Mariselvam
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp956-964

Abstract

Cognitive radio (CR) is a facilitating technology to efficiently deal with the spectrum scarceness, and it will significantly enhance the spectrum deployment of upcoming wireless transmission method. Security is a significant concern, although not well tackle in cognitive radio networks (CRN). In CR networks, this approach regard as a security issue happen from primary user emulation attack (PUEA). A PUEA attacker forwards an emulated primary signal and defraud the CR users to avoid them from accessing spectrum holes. Here, we introduce a Hermitan matrices based malicious cognitive radio (CMCR) detection and Bayesian method for detecting PUEA attack in the CRN. In this approach, the Bayesian method is used for detecting the PUEA attack. The trust analyzer evaluates the CR trust. Here, the node trust value is computed by node activeness and inactiveness, degree of data transmission, and hermitan matrics verification. In addition, the Hermitan Matrices method is used to detect the malicious CR user in the CRN. The simulation outcomes propose that the CMCR leads to improve the performance in terms of better detection ratio, minimized the possibility of miss detection ratio. Furthermore, it minimized the possibility of false alarm in the CRN.

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