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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 experimental study of tomato viral leaf diseases detection using machine learning classification techniques Sagar, Sanjeela; Singh, Jaswinder
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4385

Abstract

Agriculture is the backbone of India and more than 50% of the population is dependent on it. With the increasing demand for food with the increase in population, it is the need of time that crops should be prevented against diseases. More than 1K acres of land with tomato diseases got affected in Pune only during this pandemic (2021). It could have been prevented by correct identification of the disease and then by corrective measures. This paper presents the experimental and comparative study of tomato leaf disease classification using various traditional machine learning algorithms like random forest (RF), support vector machines (SVM), naïve bayes (NB), and deep learning convolutional neural network (CNN) algorithm. In this study, it is perceived that CNN with a pre-trained Inception v3 model was able to detect and classify better than traditional methods with more than 95% accuracy.
Chaotic-DNA system for efficient image encryption Huda Rashid Shakir; Sadiq Abdul Aziz Mehdi; Anwar Abbas Hattab
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3886

Abstract

In order to prevent unwanted access to sensitive data by unauthorized individuals, color images are encoded. Because chaotic-deoxyribonucleic acid (chaotic-DNA) encoding can make information highly secure, it is often employed in image encryption. In this study, a new image encryption technique has been proposed based on a new 4D-chaotic system and DNA computing. The algorithm consists of two phases: in the first phase, the pixel positions are permuted by chaotic sequences. In the second phase, according to the concept of DNA cryptography, a set of operations (like DNA addition, DNA XOR, DNA subtraction, shift right, and shift left) are performed on the DNA encoding sequence. The performance of the suggested algorithm is evaluated through analyses like correlation coefficient, entropy, histogram, and key space. The results show that the encryption method that was exhibited has good encryption performance and high security. For encrypted images, the histogram is fairly uniform, the correlation values between adjacent pixels are very small and close to zero, and the entropy is near to the ideal value of eight. In addition, the proposed system has a very large key space that is equal to 2627 keys, which makes it resistant to brute-force, differential, and statistical attacks.
Neuro-fuzzy-based mathematical model of dispatching of an industrial railway junction Bogdanova, Leyla M.; Nagibin, Sergey Ya; Loskutov, Dmitry I.; Goncharova, Natalia A.
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4055

Abstract

In any transport system, especially at industrial railway junctions, it is fundamentally important to build an effective timetable (traffic schedule) to regulate traffic flows. The task is complicated by the high dimensionality of the railway network of the node, the large number of variable parameters associated with scheduling the use of a traction resource (locomotives) during operation for sorting wagons and transporting payloads (ore, fuel, finished products and empty wagons). The problem is that most plotting problems are NP-hard, i.e. the algorithms for solving them, used to automate the process, may require an unacceptably long execution time by traditional methods of solving this problem (sequential, using reference information; method of thread laying). The article deals with the issues of building a mathematical model for dispatching an industrial railway junction to minimize the time of using locomotives in order to increase the efficiency of its operation. The mathematical model uses the technique of neuro-fuzzy computing to determine the parameters for identifying fuzzy systems and calculating the priorities of operations in the framework of creating a flexible schedule for the decision support system of the dispatching service. The results of modeling and recommendations on the use of the developed methodology are presented.
Reliability evaluation of distribution system integrated with distributed generation Ch. V. S. S. Sailaja; Polaki V. N. Prasad
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4534

Abstract

Distributed generation (DG) improves the reliability of the system by providing a means of alternate power supply to the load points. DG is integrated to meet the system load along with the utility. In this work roy billinton test system (RBTS) bus 4 is considered for evaluating the effect of DG integration on system reliability. Reliability is evaluated using failure modes and effect analysis (FMEA) technique and the results are validated using Monte Carlo simulation technique. A MATLAB program is developed for both the techniques to evaluate the reliability. The failure rate of DG and the islanding capability of the DG are considered to resemble the practical operating conditions of DG. When DG is operating in islanded mode, if DG fails it affects the outage time of the load points. DG failure rate is also considered as the second order failure event overlapping with the feeder section failures. All the practical operating conditions of DG considered and their effect on the reliability of the system is evaluated.
A novel microstrip fed triple band patch antenna with TM10, TM02 and TM12 induced modes Revansiddappa S. Kinagi; Ravi M. Yadahalli; Siddarama R. Patil
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4072

Abstract

In this study, a patch antenna with three different operating frequencies (2.94 GHz, 4.5 GHz, and 5.7 GHz) is proposed. The dimensions of the antenna are as follows: 23.4 mm by 30.4 mm by 1.6 mm. The triple band characteristic may be achieved by adjusting the width of the quarter wave transformer (QWT), which in turn modifies the impedance matching in such a manner as to excite the TM10, TM02, and TM12 modes. These modes are able to have additional verification provided by the current distribution on the patch. Altering the width of the QWT provides the proposed antenna with the ability to fine-tune its resonant frequencies. At the resonant frequencies, the impedance bandwidths of -10 dB are 69 MHz, 40 MHz, and 110 MHz, respectively. 5.7 GHz has a gain of 1.5 dB, while 4.5 GHz has a gain of 0.7 dB, and 2.94 GHz has a gain of 2 dB. Additionally, this antenna finds its applications in wireless systems in the S and C bands of the frequency spectrum. The antennas are validated using a vector network analyzer (VNA) after being simulated with mentor graphics software three-dimensional electromagnetic modeling (3DEM) and analyzed with it.
A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm Ramos Somya; Edi Winarko; Sigit Priyanta
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4021

Abstract

In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.
Carbon nanomaterials advancements for biomedical applications Hamza Abu Owida; Nidal M. Turab; Jamal Al-Nabulsi
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4310

Abstract

The development of new technologies has helped tremendously in delivering timely, appropriate, acceptable, and reasonably priced medical treatment. Because of developments in nanoscience, a new class of nanostructures has emerged. Nanomaterials, because of their small size, display exceptional physio-chemical capabilities such as enhanced absorption and reactivity, increased surface area, molar extinction coefficients, tunable characteristics, quantum effects, and magnetic and optical properties. Researchers are interested in carbon-based nanomaterials due to their unique chemical and physical properties, which vary in thermodynamic, biomechanical, electrical, optical, and structural aspects. Due to their inherent properties, carbon nanomaterials, including fullerenes, graphene, carbon nanotubes (CNTs), and carbon nanofibers (CNFs), have been intensively studied for biomedical applications. This article is a review of the most recent findings about the development of carbon-based nanomaterials for use in biosensing, drug delivery, and cancer therapy, among other things.
Distributed denial of service attack defense system-based auto machine learning algorithm Aljanabi, Mohammad; Hayder, Russul; Talib, Shatha; Hussien Ali, Ahmed; Mohammed, Mostafa Abdulghafoor; Sutikno, Tole
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4537

Abstract

The use of network-connected gadgets is rising quickly in the internet age, which is escalating the number of cyberattacks. The detection of distributed denial of service (DDoS) attacks is a tedious task that has necessitated the development of a number of models for its identification recently. Nonetheless, because of major fluctuations in subscriptions and traffic rates, it continues to be a difficult challenge. A novel automatic detection technique was created to address this issue in this work, which reduces the feature space and consequently minimizes the computational time and model overfitting. Data preprocessing is done first to increase the model's generalizability; then, a feature selection method is used to choose the most pertinent features to increase the accuracy of the classification process. Additionally, hyperparameter tuning-choosing the proper parameters for the learning approach-improved model performance. Finally, the support vector machine (SVM) is compatible with the optimization and the hyperparameters offered by supervised learning methods. The CICDDoS2019 dataset was used to evaluate each of these assays, and the experimental findings demonstrated that, with an accuracy of 99.95%, the suggested model performs well when compared to more modern techniques.
A battery integrated multiple input DC-DC boost converter Azuka Affam; Yonis M. Yonis Buswig; Al-Khalid Hj Othman; Norhuzaimin Julai; Hani Albalawi
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4272

Abstract

In this paper, the proposed single boost converter aims to harness more than one renewable energy (RE) input source and achieve a high voltage gain. The interleaved technique combined with voltage multiplier (VM) cells, reduced inductor current and attained high voltage transfer ratio. The boost converter possesses two unidirectional input ports and a bidirectional input port that is connected to a battery storage. The duty ratios of the power and interleaving switches are used to regulate the output voltage of the proposed converter. Three operation modes are identified, and steady state analyses of the converter are presented and discussed. The converter can store excess energy in the battery during periods of abundance and deliver power to the loads when the RE sources are low or unavailable. In addition, the output voltage is higher than that of the conventional boost converter. The converter delivered 278 V from 12 V and 24 V dual input sources. The converter operation is simulated and verified using MATLAB/Simulink.
Modified ant colony optimization with selecting and elimination customer and re-initialization for VRPTW Somkiat Kosolsombat; Chiabwoot Ratanavilisagul
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3943

Abstract

Vehicle routing problem with time windows (VRPTW) is a special kind of vehicle routing with adding time windows constraints and has a variety of applications in logistics. Many researchers have attacked the VRPTW by approximate solutions. Ant colony optimization (ACO) is a classical method to solve the VRPTW problem but the constraints of VRPTW are not used to consider customer selection. Most ACO-based optimization algorithms can suffer from the complexity of the VRPTW such as trapping in local optimum. In this paper, we present a novel ACO-based optimization method for VRPTW by using customer selection in order to decrease or solve the inefficiency of the customer selection of the ACO process. Moreover, we enhance performance searching of ACO in order to eliminate these small routes from the ACO process. Finally, we proposed the re-initialization technique in order to decrease or solve trapping in local optimum. Experiments conducted on fifty-six maps dataset have shown that the proposed method achieves encouraging performance compared to other ACOs.

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