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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 65 Documents
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Search results for , issue "Vol 12, No 4: August 2023" : 65 Documents clear
An algorithm for enhancement of audio content classification Arti V Bang; Radhika G. Purandare; Archana K. Ratnaparkhi
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.3663

Abstract

Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.
Medication correlation analysis for outbreak prediction Md Mohibullah; Meskat Jahan; Chowdhury Shahriar Muzammel; Fahim Shahriar; Raihan Khan
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4935

Abstract

Outbreak prediction is a way to predict the epidemic potentials of diseases using the pattern of medication sales values. Successful prediction might result in being cautious of the outbreak of diseases and taking necessary measures to prevent the predicted outcome. As medication sales values are too random, the analysis of medication correlation is one of the most interesting and challenging parts for the researchers. The major objective of this proposed research method is to analyze medication drug sales values for a certain period of a pharmaceutical company using statistical methods. It is also the intent of this research to make a comparative analysis of the output generated by the deep learning model with the real sales values of a month. Our method successfully predicts the outbreak potential of diseases with competent accuracy, so that we will have enough time to take precautions and prevent future pandemics through precautionary measures.
Bengali Slang detection using state-of-the-art supervised models from a given text Md. Abdul Hamid; Eteka Sultana Tumpa; Johora Akter Polin; Jabir Al Nahian; Atiqur Rahman; Nurjahan Akther Mim
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4743

Abstract

Almost all Bengalis who own smartphones also have social media accounts. People from different regions occasionally employ regional Slang that is unfamiliar to outsiders and confuses the meaning of the sentence. Nearly all languages can now be translated thanks to modern technology, but only in very basic ways, which is a concern. Bengali Slang terms are difficult to translate due to a dearth of rich corpora and frequently occurring new Slang terms developed by people, making it impossible for speakers of other languages to understand the context of a sentence in which Slang is used. We developed a solution to this issue. To create models that can detect Bengali Slang terms from social media, we gather various Slang phrases from various regions and develop a modest corpus. Our suggested method nearly always succeeds in extracting Bengali Slang terms from fresh material. We create a total of 7 supervised models and assess which is the most effective for our study. One of them has a 70% accuracy and 86% recall rate for successful identification. Our models may be linked to the social media platform's backend to restrict the use of Bengali Slang in posts, blogs, comments, and other areas.
Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark Sama Salam Samaan; Hassan Awheed Jeiad
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4711

Abstract

Recently, software defined networking (SDN) has been deployed extensively in diverse practical domains, providing a new direction in network management by separating the control plane from the data plane. Nevertheless, SDN is vulnerable to distributed denial of service (DDoS) attacks resulting from its centralized controller. Several studies have been suggested to address the DDoS attacks in SDN utilizing machine learning approaches. However, these approaches are resource-intensive and cause performance degradation since they cannot perform effectively in large-scale SDN networks that generate vast traffic statistics. To handle all these challenges, we build a DDoS attack detection model in SDN using Spark as a big data tool to overcome the limitations of conventional data processing methods. Four machine learning algorithms are employed. The decision tree (DT) is elected to be used for real-time deployment based on the performance results, which indicates that it has the best accuracy of 0.936. The model performance is compared with state-of-the-art and shows an overall better performance.
Handwritten signature identification based on MobileNets model and support vector machine classifier Israa Bashir Mohammed; Bashar Saadoon Mahdi; Mustafa Salam Kadhm
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4965

Abstract

Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.
Ultra-wide band antipodal Vivaldi antenna design using target detection algorithm for detection application Sajjad Ahmed; Ariffuddin Joret; Norshidah Katiran; Muhammad Faiz Liew Abdullah; Zahriladha Zakaria; Muhammad Suhaimi Sulong
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5081

Abstract

This work presents a technique for detecting targets between two walls using ultra-wide band (UWB) modified antipodal Vivaldi antenna (MAVA). The detection system works on principle of time domain reflectrometry (TDR) using through wall imaging (TWI). This technique utilized a vector network analyzer (VNA) to produced short and small pluses to irradiate through an antenna array system onto the wall under study. The purposed detection system operated in UWB frequency spectrum (3.1 GHz to 10.6 GHz). Furthermore, an algorithm for hidden target detection has been developed. The results of the simulation of the designed antenna revealed a significant level of penetration, demonstrating a smart advancement in detecting and imaging system, to locate hidden metallic targets with good accuracy. A signal processing technique have been employed to improve the resolution of the target image. Using computer simulation technology (CST) software, the development and optimization process of an antenna is carried out, and the parametric performance of return loss, directivity and radiation pattern is evaluated.
An efficient method for estimating energy losses in distribution's feeder Nur Diana Izzani Masdzarif; Khairul Anwar Ibrahim; Chin Kim Gan; Mau Teng Au; Kyairul Azmi Baharin; Nurul A. Emran; Zaheera Zainal Abidin
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5261

Abstract

This paper explains a simple and efficient methodology for calculating monthly energy losses (EL) using the load loss factor technique and a representative composite load profile. One of the important benefits of the proposed work is a simpler, more efficient and less rigorous method to estimate the system-wide energy loss of an extensive distribution network with reasonable accuracy. The sum of all EL provided by each feeder section is used to calculate the total feeder EL. A base case feeder with a typical cable type and power factor is used to generate regression equations, a peak power loss function to estimate the EL. A case study is then used to show the models and techniques that have been established. The result indicates a high level of agreement with the time-series load flow simulations (smaller than 10% deviations). With this model, an approach to estimate the EL of all radial feeders of various configurations and characteristics could be extended and implemented. The spreadsheet approach is ideal for completing a quick energy audit of existing distribution feeder EL and determining the sensitivity of distribution network efficiency to changes in feeder sections and load characteristics.
Reconfigurable graphene-based multi-input multi-output antenna design for THz applications Reem Hikmat Abd; Hussein A. Abdulnabi
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4956

Abstract

This paper presents a compact graphene-based multi-input multi-output (MIMO) antenna for wireless communications operating in frequency band (0.1-10) THz. This work has been performed with four ports microstrip antennas based on 37×88 μm² a silicon dioxide (Sio₂) substrate and copper on the ground layer, with high isolation by a series of unit cells of graphene selected between adjacent patches to reduce the transmission coefficient and antenna size. Graphene's chemical potential will change by changing the connected DC voltage, leading to bandwidth and resonant frequency variation. The simulation has a reflection coefficient is less than -10 dB at (4.5-10) THz of the frequency scale, mutual coupling (-15 dB), and the gain from (4.7-9) THz is (1.6-6.7254) dB. This paper aims to provide wideband, efficient and reconfigurable with simple graphene-based MIMO antenna for THz applications.
A new grid search algorithm based on XGBoost model for load forecasting Ngoc Thanh Tran; Thanh Thi Giang Tran; Tuan Anh Nguyen; Minh Binh Lam
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5016

Abstract

XGBoost is a highly effective and widely used machine learning model and its hyperparameters take an important role on the performance of the model. This paper presents a new grid search (GS) algorithm for obtaining optimal hyperparameters of the XGBoost model based on the median values of their error loss. A benchmark method used to evaluate the proposed and original GS algorithms is introduced. Datasets with measured daily electricity demand load values of Ho Chi Minh City, Vietnam and Tasmania state, Australia are analyzed for the performance of both algorithms. The error metrics, mean squared errors (MSEs), of the proposed algorithm are found to be 2,282 MW and 501 MW that are smaller than those of original algorithms, which are 2,424 MW and 537 MW in case of Ho Chi Minh City and Tasmania state, respectively. These results then verify the accuracy of the proposed algorithm.
Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review Najla Ilyana Ab Majid; Nik Noordini Nik Abd Malik; Nor Aini Zakaria
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4835

Abstract

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

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