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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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Data access control for named data of health things EL-Bakkouchi, Asmaa; EL Ghazi, Mohammed; Bouayad, Anas; Fattah, Mohammed; EL Bekkali, Moulhime
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.v13i4.6219

Abstract

The internet of health things (IoHT) represents an innovative network concept that significantly improving healthcare. However, security and privacy are the main concerns of IoHT because the transmitted health data is often sensitive data about patients’ health status, which needs to be secured and protected from unauthorized users and any leakage. Named data networking (NDN) is considered the most promising architecture for the future internet that perfectly fits with the requirements of IoHT systems, especially regarding security and privacy. In this paper, we exploit the fundamental features of NDN to design a robust system for IoHT to ensure secure communication and access to health data. This system presents a content access control model, which prevents attackers and unauthorized users from accessing health data, allows only authorized users to access these data, and prevents users from accessing “corrupted” or “fake” content. The simulation results show that the proposed mechanism slightly delays the secure retrieval of health data. However, this delay is tolerable since the mechanism protects the health data from unauthorized persons and those who try to inject untrusted data into the network.
Development and evaluation of a network intrusion detection system for DDoS attack detection using machine learning Ramachandra, Bharathi; Surekha, T. P.
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.v13i6.7565

Abstract

Distributed denial of service (DDoS) attacks involves disrupting a target system by flooding it with an immense volume of traffic originating from numerous sources. These attacks can disrupt online services, causing financial losses and reputational damage to various organizations. To combat this threat, the proposed network intrusion detection system (NIDS) utilizes machine learning (ML) algorithms trained on the KDDCup99 dataset. This dataset encompasses a diverse array of network traffic patterns, bounded by both regular traffic and various attack types. By training the NIDS on this dataset, it becomes capable of accurately identifying DDoS attacks based on their distinctive patterns. The NIDS model is constructed using ML approaches like random forest (RF), support vector machines (SVM), and naive Bayes (NB). The developed NIDS is evaluated using performance metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curve. The proposed method demonstrates the NIDS’s accuracy of about 93%, precision of 99% and recall of 92% in detecting DDoS attacks, transforming it into a valuable tool for network security in comparison with the current methods. The study contributes to the domain of network security by providing an effective NIDS solution for detecting the DDoS attacks in the wireless sensor network.
A reconfigurable switching diode loaded patch antenna for S, C, X, Ku, and K bands applications Shuriji, Mushreq Abdulhussain; Thaher, Raad Hamdan
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.v13i1.5738

Abstract

A reconfigurable patch loaded via a switching radio frequency (RF) diode has been designed, investigated, and fabricated. The fabricated microstrip is able to adjust among several frequencies. A spectrum analyzer is used to test and measure different modes of operations. The proposed antenna is offered a new solution of uncomplicated and incomplex design via eliminating the biasing circuit while maintaining the finest performance. Two fast-switching diodes have been inserted as a new electrical reconfiguration technique. Thus, the suggested antenna is skilled to function in different bands. An ultra-small size antenna of 31×21 mm2 has offered various operating frequencies such as S-band at 2.4 GHz, C-band at 4.97 GHz, 5.06 GHz, 5.18 GHz, 5.1 GHz, and 7.94 GHz, X-band at 10.73 GHz, 10.91 GHz, and 11.9 GHz, Ku-band at 13.52 GHz, 14.78 GHz, 14.97 GHz, 15.63 GHz, 16.4 GHz, 17.3 GHz, and 17.48 GHz, and K-band at 19 GHz. The manufactured reconfigurable patch antenna is proficient for several wireless technologies for instance industrial scientific medical band (ISM): Bluetooth, wireless-fidelity (Wi-Fi), ZigBee, internet of things (IoT), WiMAX, and smart power meters. Also, long term evolution (LTE), 5G applications, fixed wireless systems (FWS), and satellite communication applications.
Microstrip antenna with reflector and air gap for short range communication in 900 MHz band Shairi, Noor Azwan; Zakaria, Zahriladha; Mohd Ibrahim, Imran; Osman, Anwar Faizd
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.v13i2.5515

Abstract

This paper proposes a microstrip antenna that was made of a microstrip fed slot with a complimentary stub on a single dielectric medium. This antenna was integrated with a reflector and air gap for the application of short range communication (SRC) in a 900 MHz band. Analyses were made on the dimension of the reflector and the height of the air gap towards the antenna performance. Besides, an antenna field test was done for the propagation distance of the proposed antenna. As a result, with the antenna size of 13,770 mm2 , the measured return loss was -10.79 dB and the directivity gain was 7.44 dBi. Besides, with the effective isotropic radiated power (EIRP) of 7.44 dBm, it was predicted that at 100 m, the received signal would be around 60 to 70 dBm. Therefore, a high gain was produced by using a reflector with air gap and a compact size was achieved if compared to conventional high gain antenna designs such as Yagi Uda. Thus, it is suitable for a communication device such as the SRC application.
Control system development for monitoring nutrition of curly mustard plants in horizontal NFT hydroponic based-IoT Rusdiyana, Liza; Suhariyanto, Suhariyanto; Sampurno, Bambang; Ardiyanti Pratama, Tania
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.v13i5.7920

Abstract

Agricultural technology with a hydroponic system provides an alternative for farmers and communities who have limited land. This research aims to make innovations with a hydroponic monitoring system that can be done remotely via the internet that combines 2 systems, namely horizontal technique and nutrient film technique (NFT). The sample used in this study was curly mustard seeds. To combine the 2 systems, researchers designed a hydroponic prototype system using internet of things (IoT) in the form of smart hydroponics in the Blynk application. This research uses literature studies for research reference and flowcharts to regulate the flow of the program to be researched. The results showed that by using the IoT and the Blynk application, owners can monitor the nutrient content and pH of curly mustard greens remotely. The system automatically controls nutrients and pH according to the desired settings. In the growth control system of mustard curly, the use of smart hydroponics is proven to be better. Harvestable plants at the age of 34 days. Unlike the conventional system, the harvest period is at the age of 40–45 days. Therefore, smart hydroponics is more efficient because it shortens the harvesting time and saves labor.
Implementation of deep learning models in FPGA development board for recognition accuracy enhancement Jassim, Salah Ayad; Khider, Ibrahim
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.v13i6.7683

Abstract

Deep learning (DL) model performance is intricately tied to the quality of training, influenced by several parameters. Of these, the computing unit employed significantly impacts training efficiency. Traditional setups use central processing units (CPUs) or graphics processing units (GPUs) for DL training. This paper proposes an alternative using field programmable gate arrays (FPGAs) for DL training, leveraging their customizable and parallelizable architecture. FPGA programming allows for tailored circuit designs, optimizing DL training requirements and enabling efficient parallel processing. The use of FPGAs in DL training has garnered attention for their potential in achieving high computational throughput and energy efficiency, attributed to advantages like low latency, high bandwidth, and reconfigurability. By exploiting FPGA parallel processing capabilities, faster training times and the potential for larger, more complex DL models are feasible. This paper provides an overview of state-of-the-art techniques for FPGA-based DL model training, discussing challenges such as hardware architecture design, memory management, and algorithm optimization. Additionally, various FPGA-based DL frameworks and libraries facilitating DL model development and deployment on FPGAs are explored.
Stability analysis of a сlosed non-linear system “FC-BM” of the electric drive of an electric vehicle Z. Yerlan, Amangaliyev; I. Yuri, Shadkhin
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.v13i1.5653

Abstract

The article presents the analysis and stability program of the closed non-linear system frequency converter-brushless motor “(FC-BM)”, which differs significantly from the analysis of linear systems. First of all, this is because the stability property of a nonlinear system depends on the initial conditions and external influences: for some input signals, the system will be stable, while for others it becomes unstable. Consequently, the stability criteria developed in the linear theory cannot be applied to their analysis. The stability of a non-linear automatic control system means that small changes in the input signal or disturbances, initial conditions, or plant parameters will not take the output variable beyond a sufficiently small neighborhood of the equilibrium point or limit cycle. Since several equilibrium positions can exist for a non-linear system, stability should be analyzed in the vicinity of each of them. This complicates the task of research.
A deep learning-based system for accurate diagnosis of pelvic bone tumors Shouman, Mona; Rahouma, Kamel Hussein; Hamed, Hesham Fathy Aly
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.v13i3.6861

Abstract

Bone image analysis and categorizing bone cancers have both seen advancements thanks to deep learning (DL), more notably convolution neural networks (CNN). This study suggests a brand-new CNN-based methodology for categorizing pelvic bone tumors specifically. This work aims to create a pelvic bone computed tomography (CT) image categorization system based on deep learning. The proposed technique uses a convolutional neural network (CNN) architecture to automatically extract information from the CT images and classify them into distinct categories of tumors. A total of 178 3D CT pictures was discovered and added retroactively. DenseNet created the image-based model with Adam optimizer and cross entropy loss. The suggested system's accuracy is assessed using a variety of performance indicators, including sensitivity, specificity, and F1-score. As demonstrated by the experiment findings, the suggested deep learning based classification system has a high degree of accuracy (94%), making it useful for the diagnosis and treatment of pelvic bone tumors. Our promising results might hasten the use of DL-assisted CT diagnosis for pelvic bone tumors in the future.
Enhancing spyware detection by utilizing decision trees with hyperparameter optimization Abualhaj, Mosleh M.; Al-Shamayleh, Ahmad Sami; Munther, Alhamza; Alkhatib, Sumaya Nabil; Hiari, Mohammad O.; Anbar, Mohammed
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.v13i5.7939

Abstract

In the realm of cybersecurity, spyware has emerged as a formidable adversary due to its persistent and stealthy nature. This study delves deeply into the multifaceted impact of spyware, meticulously examining its implications for individuals and organizations. This work introduces a systematic approach to spyware detection, leveraging decision trees (DT), a machine-learning classifier renowned for its analytical prowess. A pivotal aspect of this research involves the meticulous optimization of DT's hyperparameters, a critical operation for enhancing the precision of spyware threat identification. To evaluate the efficacy of the proposed methodology, the study employs the Obfuscated-MalMem2022 dataset, well-regarded for its comprehensive and detailed spyware-related data. The model is implemented using the Python programming language. Significantly, the findings of this study consistently demonstrate the superiority of the DT classifier over other methods. With an accuracy rate of 99.97%, the DT proves its exceptional effectiveness in detecting spyware, particularly in the face of more intricate threats. By advancing our understanding of spyware and providing a potent detection mechanism, this research equips cybersecurity professionals with a valuable tool to combat this persistent online menace.
An ANN enabled joint power allocation and base station switching system for EE heterogeneous networks Euttamarajah, Shornalatha; Ng, Yin Hoe; Tan, Chee Keong
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.v13i6.7262

Abstract

In recent years, dynamic and complex development in wireless communication in network models or environments led to more tedious and complicated resource management issues (i.e., power allocation and base station switching (BSS)). Conventional solutions often suffer from delays and degraded network service quality. Due to the ability of machine learning in analyzing huge volumes of data and automatically adapt to environmental changes, it emerges as a highly sought-after technique. In this work, we propose a machine learning approach based on feed-forward neural network (FFNN) to predict the active BS sets and estimate the power allocation to each user equipment (UE) within the active BSs for energy-efficiency (EE) maximization of a coordinated multi-point (CoMP-enabled) cellular system with hybrid-powered transmitting nodes in a HetNet-based architecture. By training the neural network model efficiently using a regression-based supervised learning technique that employs various backpropagation algorithms, almost similar EE performance (less than 5% difference) can be achieved with significantly reduced computational complexity and delay compared to the traditional methods, such as the well-known dual decomposition and brute force techniques. The effects of various hyper parameters and back-propagation algorithms are also investigated. Our results demonstrate that the proposed framework is a promising solution for establishing a fully green and intelligent network.

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