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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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An efficient intrusion detection systems in fog computing using forward selection and BiLSTM Abu Zwayed, Fadi; Anbar, Mohammed; Manickam, Selvakumar; Sanjalawe, Yousef; Alrababah, Hamza; Hasbullah, Iznan H.; Almi’ani, Noor
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7143

Abstract

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.
Handwritten Arabic words detection using Faster R-CNN in IFN/ENIT dataset Mowaffaq AL-Taee, May; Ben Hassen Neji, Sonia; Frikha, Mondher
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8189

Abstract

Recognizing Arabic offline handwritten words still faces various challenges because of the diversity of writing styles and the overlap between the words and characters. Therefore, building an effective system to solve these challenges has always been difficult, which has led to a lack of published research in this field. This study introduces two new models to recognize handwritten Arabic words based on the Faster region-convolution neural network (Faster R-CNN). These models employ two pre-trained networks during the feature extraction phase: The visual geometry group-16 (VGG-16) network and the residual network (ResNet50) network. To help with overlapping detections and make localization more accurate, a soft non-maximum suppression (Soft-NMS) strategy is used in post-processing. Models are independently trained and tested on two groups of data from the Institut Für Nachrichtentechnik/Ecole Nationale d’Ingénieurs de Tunis (IFN/ENIT) dataset. The first group includes one word in each image, while the second contains multiple words. Test results showed that the proposed models give excellent results compared to others. The results of VGG16 and ResNet50 with the first dataset reached accuracy rates of 100% and 99.5%, respectively. Meanwhile, the accuracy of the second group reached 91.4% and 100% with VGG16 and ResNet50, respectively.
The effect of FeNi-AlN layer thickness on the response of magnetic SAW sensor by FEM simulation Phu, Do Duy; Hoang, Hong Si; Van Vinh, Le
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6312

Abstract

In this study, we used simulation to investigate the optimal working point of a surface acoustic wave-magnetostriction sensor by varying the thickness of the magnetic sensitive layer using the finite elements method. We evaluated the sensor’s sensitivity by simulating the responses at the optimal point and changing the thickness of the magnetic sensitive layer (h3). Additionally, we reduced the piezoelectric substrate thickness (h1) at the optimal point to determine the limit point of the center frequency (f0) and improve the sensor sensitivity for low magnetic field intensity measurements by performing a wavelength reduction (λ). For the simulation, we selected a delay-line FeNi/IDT/AlN structure with specific materials and electrode parameters. Our results show that the optimal structure of the sensor is at h1=400 μm, λ=40 μm, and h3=1,060 nm, with a maximum f0 of 140.38493 MHz and maximum surface acoustic wave velocity of 5,615.4 m/s. At this optimal structure, the sensitivity reaches the maximum value of 10.287 kHz/Oe with a working range from 0 to 89 Oe. We also found that reducing the piezoelectric substrate thickness to 35 μm significantly reduces the manufacturing and simulation time, but the frequency response cannot determine the center frequency.
Fifth generation core: the performance enhancement of virtual private server and bare metal Putri, Hasanah; Hikmaturokhman, Alfin; Ahmad, Izanoordina; Anwar, Radial; Akbar, Rafli
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6889

Abstract

The fifth generation (5G) architecture represents the most recent advancement in mobile networks and is presently operational in various global places. Several new use cases and applications have been introduced, with a specific focus on improving throughput, reducing latency, minimising packet loss, optimising CPU usage, and maximising memory utilisation. In order to effectively address each scenario, it is necessary to integrate the most advanced technology, putting in significant effort to optimise resources and ensure system adaptability. This strategy will establish an architecture capable of accommodating many scenarios of a shared physical infrastructure by using techniques such as virtualization and cloud-based service deployment. Therefore, in this study, a test was carried out related to the performance of the 5G core network (CN) on bare metal servers and virtual private servers (VPSs). The quality of service (QoS) using Wireshark and Iperf3 is tested by utilizing ‘cpustat’ and free tools. The results of performance comparisons of these two methods on the 5G CN shows throughput values of ≥10 Gbps ≤20 Gbps, latency values of ≤4 ms, and packet loss values of 0%, in accordance with IMT 2020 standards. Thus, the ideal 5G CN services can be realized.
Deep learning based detection, classification, and location of power system faults Sahoo, Anjan Kumar; Samal, Sudhansu Kumar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7239

Abstract

The identification, categorization, and localization of faults play a crucial role in maintaining the smooth operation of power systems. Distance relays possess a significant capability to withstand power fluctuations, thereby minimizing inadvertent disruptions in transmission lines. Addressing these challenges involves the adoption of advanced fault analysis techniques to enhance the accuracy and speed of relay operations. While modern machine learning (ML) approaches are still nascent in fault analysis, the authors propose a novel deep learning (DL) based long short term memory (LSTM) method for precise fault detection, classification, and rapid fault location estimation. The proposed approach is applied to the Kundur two-area 4 machine 11 bus system covering a distance of 220 km. The LSTM fault detection (LSTM (FD)) module accurately detects and classifies faults, while the LSTM fault location (LSTM (FL)) module precisely estimates fault locations. The effectiveness of the proposed method is verified through a comparative assessment with various traditional ML and DL techniques. The protection modules are also tested under different fault locations, fault resistances, and noisy signals. The features taken into consideration for the operation of the protection modules are different bus voltages, bus currents, zero sequence voltage, zero sequence current, fault inception angle, and fault resistance.
Machine learning prediction for academic misconduct prediction: an analysis of binary classification metrics Masrom, Suraya; Abdul Samad, Nor Hafiza; Septiyanti, Ratna; Roslan, Nurshafinas; Rahman, Rahayu Abdul
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5629

Abstract

Academic misconduct is unethical behavior in academic work. To sustain integrity culture and mitigating unethical conducts among higher education institutions community, the academic misconduct detection must be done at an earlier stage. Thus, this study attempted to provide a new empirical contribution with the analysis of binary classification performances metrics to describe the ability of machine learning in predicting academic misconduct. Four machine learning algorithms have been used namely generalized linear model (GLM), logistic regression (LR), decision tree (DT), and random forest (RF). Beside performances comparison, this paper presents the analysis of academic misconduct factors that were constructed based on demography and fraud triangle theory (FTT). The findings showed that all the four machine learning algorithms have obtained good ability in the prediction models with the accuracy at above 80% and below 20% of the classification errors. Rationalization from the FTT attributes has shown as the most important factor in GLM, LR, and DT. In RF, opportunity of FTT attributes have become the most important. Compared to FTT attributes, demography attributes were not providing much benefits to all the machine learning models but remain applicable at very low weight correlations.
Secure Euclidean random distribution for patients’ magnetic resonance imaging privacy protection Tayh Albderi, Ali Jaber; Ben Said, Lamjed
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5989

Abstract

Patients’ information and images transfer among medical institutes represent a major tool for delivering better healthcare services. However, privacy and security for healthcare information are big challenges in telemedicine. Evidently, even a small change in patients’ information might lead to wrong diagnosis. This paper suggests a new model for hiding patient information inside magnetic resonance imaging (MRI) cover image based on Euclidean distribution. Both least signification bit (LSB) and most signification bit (MSB) techniques are implemented for the physical hiding. A new method is proposed with a very high level of security information based on distributing the secret text in a random way on the cover image. Experimentally, the proposed method has high peak signal to noise ratio (PSNR), structural similarity index metric (SSIM) and reduced mean square error (MSE). Finally, the obtained results are compared with approaches in the last five years and found to be better by increasing the security for patient information for telemedicine.
Performance evaluation of software defined networking into vanets system Taher, Younus Hasan; Alsaadi, Israa; Saad, Mohammed Ayad; Ali, Adnan Hussein; Essa, Mohammed; Rashid, Ahmed Hashim
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.4675

Abstract

Vehicular ad hoc networks (VANETs) is an important topic nowadays. A lot of research deal and attracts consideration owing to potential for increasing traffic and travel efficiency, improving road safety for vehicles, providing convenience and comfort to both drivers and passengers. The need for a packet delivery ratio (PDR) and low delivery delay time in communication are the key elements in modern life especially when traveling in vehicles. To satisfy these demands; researchs in VANET systems aims to develop some new technologies. One of these technologies is using software-defined- network (SDN) to enhance communication between vehicles on the road. Because of this, project evaluates using SDN protocol with two most viable VANET protocols which are ad hoc on demand distance vector (AODV) and optimized link state routing (OLSR) in LTE communication. Two performance metrics are used to evaluate the performances, the PDR and the delivery delay time. The simulation is performed in the varying density network and varying speed vehicles. The simulation results show that SDN displays better performance than AODV and OLSR in both PDR and delivery delay time. SDN uses global views of SDN controller to determine the shortest route with the highest vehicle density. Additionally, it solves the local maximum issue and adds dense connectivity.
An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people Kumer Paul, Subrata; Ala Walid, Md. Abul; Rani Paul, Rakhi; Uddin, Md. Jamal; Rana, Md. Sohel; Kumar Devnath, Maloy; Rahman Dipu, Ishaat; Haque, Md. Momenul
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6059

Abstract

Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.
Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon Kannan, Deeba; Balakrishnan, Amutha; Devi, K. Mekala; Singh, Nagendra; Kiruba, P. Angelin; Ramkumar, Ravindran; Karthikeyan, Dhandapani
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5432

Abstract

Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant.

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