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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 72 Documents
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Search results for , issue "Vol 13, No 1: February 2024" : 72 Documents clear
Tomato pest recognition using convolutional neural network in Bangladesh Polin, Johora Akter; Hasan, Nahid; Habib, Md. Tarek; Rahman, Atiqur; Vasha, Zannatun Nayem; Sharma, Bidyut
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.v13i1.6073

Abstract

The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.
Optimization of solar powered air conditioning system using alternating Peltier power supply Salman, Mustafa Mohammed; Mahdi, Mahmoud Mustafa; Ahmed, Majida Khalil
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.v13i1.5864

Abstract

Solar-powered thermoelectric air conditioning systems offer distinct advantages over traditional cooling methods, including thermal comfort, absence of moving parts, and eco-friendliness as they operate on solar energy. Despite these benefits, they exhibit a lower coefficient of performance (COP) compared to conventional systems. In this study, a solar-powered thermoelectric air conditioning system based on the Peltier effect was experimentally investigated in Baghdad during September (39 °C to 32 °C). The system was designed to cool a small 1 m³ test room. The six Peltier modules were divided into groups, each powered by a different electrical source with varying ON/OFF intervals. The highest COP achieved was 0.649, with an optimal outlet air temperature of 22-23 °C and a 20 minute switching cycle. Notably, the inlet air velocity directly influenced COP and outgoing air temperature. The study also indicated improved performance at reduced air flow, making Peltier air coolers ideal for hot regions.
The hybrid solar energized back-to-back high voltage direct current modular converter for distributed networks Thadkapally, Karunakar; Josh, Francisxavier Thomas; Joseph, Jeyaraj Jency; Jayakumar, Jayaraj
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.v13i1.5771

Abstract

High voltage direct current (HVDC) transmission is flexible towards the power control (produced by solar or wind) and can be transported over thousands of kilo meters with minimal losses over the high voltage alternative current (HVAC). It allows solar power to be integrated into the current power grid on a large scale. The author view in this article aims at providing an overview of methods used to integrate HVDC and solar systems. MATLAB/Simulink is used to simulate the solar power integration with HVDC transmission link. This article emphaises solar energy and grid integration, which results in quality and controlled electricity to the grid. Further the simulation studies are compared with real time data between the stations Pugalur AC grid (high solar energy region) and Thrissur AC grid (low solar energy region). Obtained results from the simulation, voltage and currents and power quality stresses the superiority towards the solar integration. The comparison studies enumerate the need to go situation for HVDC technology during the penetration of solar voltaic penetration into the utility network.
Improving skin diseases prediction through data balancing via classes weighting and transfer learning El Gannour, Oussama; Hamida, Soufiane; Lamalem, Yasser; Mahjoubi, Mohamed Amine; Cherradi, Bouchaib; Raihani, Abdelhadi
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.v13i1.5999

Abstract

Skin disease prediction using artificial intelligence has shown great potential in improving early diagnosis and treatment outcomes. However, the presence of class imbalance within skin disease datasets poses a significant challenge for accurate prediction, particularly for rare diseases. This study proposes a novel approach to address class imbalance through data balancing using classes weighting, coupled with transfer learning techniques, to enhance the performance of skin disease prediction models. Two experiments were conducted using a tuned EfficientNetV2L based classifier. In the first experiment, a default dataset structure was utilized for training and testing. The second experiment involved employing classes weighting approach to balance the dataset. The effectiveness of the proposed approach is evaluated using the ISIC 2018 dataset, which comprises a diverse collection of skin lesion images. By assigning appropriate weights to different classes based on their prevalence, the proposed method aims to balance the representation of rare disease classes. To evaluate the performance of the proposed methodology, several performance evaluation metrics, including accuracy, precision, and recall, were employed. These findings revealed that the balanced dataset achieved enhanced generalization, mitigating the biases associated with class imbalance. As a result, the efficacy of artificial intelligence models is enhanced.
High capacity double precision image steganography based on chaotic maps Al Rubaie, Salwan Fadhel; Al-Azawi, Maher K. Mahmood
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.v13i1.6055

Abstract

Steganography is the process of hiding confidential information within non-secret multimedia such that the 3rd party cannot distinguish if there is a secret message in it or not. Whereas cryptography is the technique of using mathematical concepts to convert information into unreadable codes via a key. This paper will propose two approaches, lossless and lossy image steganography. Both of them will use cryptography and steganography based on three different chaotic maps to ensure information security. In the cryptography part, two chaotic maps will be used to encrypt the secret information, while in the steganography section, one chaotic map is used to embed the message. The secret information will be concealed in the least significant bits (LSBs) of the double-precision image’s pixels. The double precision image is a high-quality image and can be represented in 64 bits per pixel for grayscale images, leading to a very high redundant bit. Simulation results show a high embedding capacity of 60.938% and 400% for lossless and lossy approaches respectively with a peak signal to noise ratio (PSNR) reach of 69.964 dB. Furthermore, this system is extremely secure due to the use of 3 chaotic maps with key space 2448.
Securing laboratories through internet of things networks: a comprehensive approach for ensuring safety and efficiency Abderrahmane, Tamali; Amardjia, Nourredine; Mohammed, Tamali
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.v13i1.6728

Abstract

The design of a secure, intelligent laboratory that incorporates internet of things (IoT) devices and applications is a complex process. One of the main goals is to create a process monitoring system that can collect and analyze data from connected devices such as temperature and pressure sensors, smart locks, and access control systems. This system must operate in real time to ensure that equipment is within reference values. This reduces the risk of contamination and increases reliability. In addition, computer network security is paramount and it is imperative that certain measures such as encryption, multi-factor authentication, and intrusion detection systems are implemented. These measures help to ensure the safety and security of critical information and protect against potential risks. Physical security is also essential to protect scientific equipment and data. This paper provides a comprehensive overview of the critical factors involved in designing a secure, intelligent laboratory. It discusses the benefits of the integration of IoT devices and applications, and the security challenges that must be addressed. The paper also provides recommendations for designing and implementing a secure smart lab.
Implementing and developing multi-stage cryptography technique for low-cost long-range communication system Hamad, Eyad M.; Alabed, Samer; Alsaraira, Amer; Saraereh, Omar A.
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.v13i1.6989

Abstract

The requirement for a secure emergency communication system has become imperative in tandem with the industrial revolution. Additionally, the development of technology has led to increasingly robust penetration techniques that pose a threat to communication system security, leaving data vulnerable to unwanted third parties. This paper introduces a novel, powerful security approach that ensures a secure emergency communication system. Moreover, this research focuses on several cryptographic techniques among various symmetric and asymmetric ciphers, including advanced encryption standards, substitution, and transposition. The article presents an affordable and secure communication system that can transmit data over long distances with low power consumption using long-range technology. This system features a unique function that transmits updated locations, directing rescuers to the designated location.
Effect of shunt reactor rating on the switching transients overvoltage in high voltage system Al-Tak, Mazyed A.; Ain, Mohd Fadzil; Al-Yozbaky, Omar Sh.; Mohd Jamil, Mohamad Kamarol
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.v13i1.6100

Abstract

This paper includes a computer simulation of switching transient overvoltages during the disconnecting of shunt reactors (SR). Based on the results of the transient simulations, two reactors (150 MVAr and 50 MVAr) were examined. Reactor current interruption causes significant overvoltages, especially across the reactor and the circuit breaker (CB). The severity of these overvoltages may surpass the voltage level, which might endanger the reactor’s insulation and accelerate the CBs ageing process. For this reason, a significantly modified circuit model is proposed. The results of the field testing showed that the proposed modified circuit technique was successful. The transient overvoltages were computed using ATP-draw simulations of the equivalent circuit during switching. Successful synchronous switching could decrease the electromagnetic and mechanical stress generated during frequent switching operations. The model was examined the switching transient overvoltage for different values of current chopping (0-20) A. The proposed model proved that the reduction of the overvoltage was 86% in case of (50 MVAr) shunt reactor rating and 87% in case of (150 MVAr) shunt reactor rating.
Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy Singsathid, Pirapong; Wetweerapong, Jeerayut; Puphasuk, Pikul
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.v13i1.6590

Abstract

Differential evolution (DE) is a population-based optimization algorithm widely used to solve a variety of continuous optimization problems. The self-adaptive DE algorithm improves the DE by encoding individual parameters to produce and propagate better solutions. This paper proposes a self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The algorithm introduces the dynamical mutation operation using the fitness rank of the individuals to divide the population into three groups and then select groups and their vectors with adaptive probabilities to create a mutant vector. Mutation and crossover operations use the encoded scaling factor and the crossover rate values in a target vector to generate the corresponding trial vector. The values are changed according to the pheromone when the trial vector is inferior in the selection, whereas the pheromone is increased when the trial vector is superior. In addition, the algorithm also employs the resetting operation to unlearn and relearn the dominant pheromone values in the progressing search. The proposed SDE-FMP algorithm using the suitable resetting periods is compared with the well-known adaptive DE algorithms on several test problems. The results show that SDE-FMP can give high-precision solutions and outperforms the compared methods.
Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system Ali Al Essa, Hasanain; S. Bhaya, Wesam
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.v13i1.5844

Abstract

Feature selection (FS) plays an important role in the construction of efficient ensemble classifiers; particularly for intrusion detection system (IDS). An IDS is a utilized in a network architecture to protect the availability of sensitive information. However, existing IDSs suffer from redundancy, high dimensionality, and high false alarm rate (FAR). Also, lots of models are constructed for outdated datasets, which makes them less flexible to deal with new assaults. Therefore, this paper proposes a new IDS relies on hybrid FS and ensemble classifiers. A hybrid FS approach consists of two techniques, hard-voting and mean. In contrast to recent papers, we use three different FS approaches: extra tree classifier importance as an embedded FS, recursive feature elimination (RFE) as a wrapper FS, and mutual information (MI) as a filter FS. Then, a hard-voting technique has been used to fuse output of these approaches and obtain a reduced subset of features. Since each feature has three weights, a mean technique has been utilized to assign one weight to each feature and obtain an optimal subset of features. The experimental outcomes, utilizing the modern InSDN dataset, confirm that the proposed hybrid FS with ensemble soft voting classifier achieves better results than other ensemble and individual classifiers due to several measures.

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