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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 31, No 3: September 2023" : 64 Documents clear
Design of intelligent agent on Moodle to automate the learning assessment process Elhoucine Ouassam; Nabil Hmina; Belaid Bouikhalene
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1665-1672

Abstract

Assessment is a key element in today’s School system, whether face-to-face or distance learning, as it helps students understand their learning and get feedback on their progress. In addition, distance learning assessment is becoming increasingly popular as it is convenient for students with busy schedules who cannot attend face-to-face assessments. In this paper, we focus on the use of intelligent agents on the Moodle platform to improve the assessment process of distance learning. We present three contributions that aim to improve the developed models: firstly, the digitisation of assessment to collect, store and analyse data; secondly, the adoption of a multi-agent skills assessment environment to automate some assessment tasks; thirdly, the adoption of the leadership and management development (LMD) programme to improve the continuous training of learners by offering greater flexibility, adaptability and relevance to their needs. 
Throughput maximization with channel access fairness model using game theory approach Humera Tauseef; Rukhsar Fatima; Rohina Khanam
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1319-1327

Abstract

The adoption of cognitive radio (CR) technology into wireless sensor networks (WSNs) effectively addresses the spectrum scarcity problem of traditional unlicensed spectrum. Allocating and managing limited network channel to secondary user (SUs) considering dynamic behavior pattern of primary users (PUs) is a critical issue of CR-WSN. Recently, various channel access methodologies using statistical, reinforcement learning (RL), game theory (GT), and deep learning (DL) model have been presented for CR-WSN. However, the existing channel access methodologies has following two limitations: i) fails to assure balance between maximizing throughput of SUs with minimal interference to PUs considering multi-channel CR-WSNs environment; and ii) maximizing throughput with minimal collision assuring access fairness among SUs considering energy constraint CR-WSNs. In addressing the research issues, this paper present throughput maximization channel access fairness using game theory (TMCAF-GT) model. The TMCAF employ both shared and non-shared channel access mechanism employing GT model for assuring throughput maximization with minimal interference and access fairness. Experiment outcome shows the TMCAF-GT provides superior throughput with minimal collision.
Application of artificial neural networks for personality traits prediction based on handwriting Ahmed Remaida; Benyoussef Abdellaoui; Mohamed Amine Lafraxo; Zineb Sabri; Hamza Nouib; Younes El Bouzekri El Idrissi; Aniss Moumen
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1534-1544

Abstract

The automatization of personality traits prediction still brings considerable research these days, especially when the detection could be achieved without administrating personality assessment instruments. With the evolution of computational intelligence, a variety of deep machine learning techniques were developed and proposed for that purpose. Nevertheless, proposing robust and rapid systems to solve this problem remains a challenging task. The process of feature extraction is the main key. This paper presents an effective method for extracting five graphological features from handwriting ensuring the prediction of personality traits based on the big five personality traits model. We started by collecting both handwriting samples and big five questionnaires, then the feature extraction process, after that the data preparation and finished with the application of several popular deep machine learning models to achieve the prediction. Experimental results indicate the remarkable performance of the multi-layer perceptron (MLP) compared to other classifiers, the model was 100% precise and classification accuracy attained 100% for trained data and 72.73% for new tested data. With only 100 participants, we strongly believe that our proposed method is simple and promising, and better results will be attained with a larger dataset.
UAV-enabled communications using NOMA for 5G and beyond: research challenges and opportunities Muhamad Nafis Kharil; Muhammad Syahrir Johal; Fakrulradzi Idris; Norlezah Hashim
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1420-1432

Abstract

Future wireless networks are expected to provide ubiquitous connectivity to a wide range of devices with varying traffic patterns, wherever and whenever it is required. Unmanned aerial vehicles (UAVs) are also considered a potential technique for accommodating massive connections and providing seamless coverage. They can be used as flying base stations (BSs) to take advantage of line-of-sight (LoS) connections and effectively support wireless communication coverage and throughput for 5G and beyond. However, the use of highly mobile and energy-constrained UAVs for wireless communications brings plenty of new challenges. 5G wireless networks require non-orthogonal multiple access (NOMA) to be able to meet heterogeneous requirements for low latency, high dependability, massive connection, better fairness, and high throughput. This paper presents an overview of NOMA-based UAV enabled communications by introducing the background of UAV communication and NOMA schemes. Power allocation schemes are also explored as they are critical controlling mechanisms for performance optimization of NOMA-UAV systems. We also categorize UAV-enabled communication applications for usage in both routine professional settings and emergency scenarios. Finally, we address several open research questions that need to be solved for NOMA, as well as new opportunities and future research trends to be exploited.
Enhancement of luminous efficacy for light-emitting diodes lamps by adding CaSr2(PO4)2:Eu3+ phosphor Ho Minh Trung; My Hanh Nguyen Thi
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1328-1333

Abstract

Through employing the fluid ignition technique, we created the samples CaSr2(PO4)2:Eu3+ with Eu3+ incorporated then assessed them in the form of contactless optical heat measurement as well as solid-state illumination. We identified the attributes for these samples including X-ray diffraction (XRD), photoluminescent spectroscopy, Fourier transmute infrared spectroscopy (FTIS) along with photoluminescent spectroscopy correlating with temperature. XRD, along with FTIS, validates that the one-stage samples were formed via the orthophosphate anion (PO4)3- . In the case of these samples, nUV recreation under 395 nm generated potent, orange-red discharge lines under 592 nm as well as 615 nm, which is consistent with the standard shifts between 5D0 and 7F1 as well as 5D0 and 7F2 for the Eu3+ ions. The International Commission on Illumination (CIE) coordinates (0.65, 0.35) based on the hue scale validate the red discharge. For the task of attaining optical heat measurement, we took advantage of the fluorescent intenseness proportion technique that utilizes heat-incorporated discharge states of 5D1 as well as 5D0 for Eu3+ . The samples have maximum responsiveness reaching roughly 0.0023 K-1 under 323 K or small heat levels. According to the outcomes, it is possible to utilize the samples when it comes to contactless optical heat measurement as well as solid-state illumination.
Real-time optimized wireless networked control system with cooperative network protocols Yousif Safaa Alobaidy; Osama Ali Awad
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1350-1361

Abstract

In this paper, we present a real-time optimized fuzzy fuzzy proportional integral derivative (FPID)-controlled wireless networked system for a high-torque direct current (DC) motor. The main challenge faced by such systems is the delay in the wireless networked control system (WNCS). We employed a powerful FPID controller tuned using particle swarm optimization (PSO) technique to compensate for the delay. The system is tested on a network using the TrueTime simulator with different parameters. The results show that the system exhibits a very stable response, with the FPID controller compensating for the delay effectively. Increasing the number of nodes negatively impacts the system's performance, resulting in higher overshoot, longer settling time, and longer rise time. Moreover, the choice of bandwidth share and sampling time significantly affects the system's stability and real-time response. The use of transmission control protocol/internet protocol (TCP/IP) or user datagram protocol (UDP) protocols with Node MCU is necessary to transfer data from the Arduino Microcontroller to MATLAB, as MATLAB TrueTime simulator does not support direct serial communication. In conclusion, this study highlights valuable insights into the performance of the proposed system, demonstrating the need for further improvements in the system's design and control algorithms to achieve stable operation.
Intelligence feeder system for stray cats Alaa Mohsin Saadoon; Hakan Koyuncu
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1507-1514

Abstract

Recently, the care of stray cats has become an important matter, given the difficulty for these animals to obtain food or water, whether in wild or remote areas, and this may cause the death of these animals. The aim of this work is to design and implement a low-cost feeding system for feral and indoor cats. The system is controlled by a jetson nano and arduino mega 2,560. The cat detection feeder system is built using the single shot multibox detector (SSD) MobileNet V2 algorithm. The system provides food and water for the cats. The SSD on jetson nano is implemented in real time. Jetson nano takes a picture using a webcam then runs the SSD algorithm. When a cat is detected, the arduino turns on a servo motor and a water pump. The system also includes a sim800l and GPS NEO-6M modules to send an alert message when the food and water tanks are empty. The message also contains the location of the feeder. When testing the system to determine the effectiveness of the functions, the SSD algorithm succeeded in recognizing cats, the system successfully provided food for the cats, and all parts of the device worked in high harmonic.
Social mobility and geo-context aware macroscopic routing scheme for mobile opportunistic network Shobha R. Bharamagoudar; Shivakumar V. Saboji
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1724-1734

Abstract

Mobile opportunistic networks (MONs) are deliberated as important aspect to proliferate the wireless communications. These networks pose several challenges such as network lifetime, storage capacity, and forwarding capacity. End-to-End routing schemes are considered as promising technique solution to deal with these issues. In this domain, opportunistic routing has gained huge attention because it follows the broadcasting nature of wireless communication and focus on selection of relay node for packet transmission to ensure the better quality of service (QoS) and energy efficiency. This work focuses on optimizing the next hop selection process and introduced reinforcement learning approach which considers distance, energy and link connectivity to assign the reward for different actions to identify the suitable relay node. Moreover, geo-context and social behaviour based opportunistic routing models are used to increase the reliability of next hop selection. Similarly, social model considers social profiling, social connectivity, and social interaction model to identify the relay node. The outcome of proposed approach is compared with several existing approaches such as prophet, spray and wait, and epidemic routing in terms of packet delivery, and network overhead. The relative study shows that the proposed approach achieves the average packet delivery as 47.22% and minimizes the network overhead.
Identification of mango variety using near infrared spectroscopy Mukesh Kumar Tripathi; Praveen Kumar Reddy; M. Neelakantappa; Chetan Vikram Andhare; Shivendra Shivendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1776-1783

Abstract

The structure of the proposed framework is separated into three stages: i) foundation deduction, ii) component extraction, and iii) preparing and characterization. At first, K-implies grouping methods were carried out for foundation de- duction. The second step applies color, texture, and shape-based feature extraction methods. Finally, a “merging” fusion feature is analyzed with a C4.5, support vector machine (SVM), and K-nearest neighbors (KNN). Overall, the recognition system produces an adequate performance accuracy with 97.89, 94.60, and 90.25 percent values by utilizing C4.5, SVM, and KNN, respectively. The experimentation points out that the proposed fusion scheme can significantly support accurately recognizing various fruits and vegetables.
Design of inception architecture for skin melanoma classification Sankarakutti Palanichamy Manikandan; Vedanandam Karthikeyan; Ganesamoorthy Nalinashini
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1372-1381

Abstract

The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods of examination may be used to investigate it. However, the invasive method is more difficult for the patient because samples must be taken from the lesion itself, or the whole lesion must be cut out. It also requires more time and cost. To avoid invasive procedures, computer-based analysis and diagnosis have the potential to increase diagnostic accuracy and turnaround time. This study develops a unique discriminative deep learning architecture (DDLA) for dermoscopic image classification (DIC), called DDLA-DIC, which uses the concept of inception. Using this concept, the proposed DDLA-DIC system is designed wider and deeper and the network learns from various spatial patterns. The proposed DDLA-DIC system can extract image characteristics from dermoscopic images for skin cancer diagnosis in an effective and efficient way. The proposed DDLA-DIC system is evaluated by utilizing the dermoscopic images from the PH2 database, and the obtained classification results are based on a random split approach. The simulation results indicate that the framework has a great deal of potential with 99.79% accuracy.

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