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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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Evaluating drones as bird deterrents in industrial environments: multirotor vs fixed-wing efficacy Hornain, Imran Mohd; Rosely, Nik Fadzly N
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.v13i6.7359

Abstract

Unmanned aerial vehicles (UAVs) or drones have been proposed as deterrent tools to mitigate pest birds’ problems. Many studies have been conducted to evaluate the efficacy of drones, mainly to protect crops, fishponds and airports. Little information can be acquired on using drones in industrial areas. In this study, two types of drones, categorized as multirotor drones and fixed-wing drones, were used to evaluate their efficacy in reducing pest birds, Asian glossy starling (Aplonis panayensis) flocks in one of the semiconductor factories in Kulim Hi-tech Park, Kedah, Malaysia during dusk. Each drone was evaluated during its five minutes of operation time and five minutes after landing. Control data were also taken to compare drone treatment days with no drone treatment days. Our result shows a significant difference between multirotor drone treatment and control treatment but not between fixed-wing drone treatment and control treatment due to different altitudes applied, ambient light intensity and size of flight path covered. We suggest implementing biomimetic design into drones and applying other conventional ground deterrents to prolong the residual effect of post-treatment.
Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network Al-Heeti, Mohamed Muthanna; Hammad, Jamal A.; Mustafa, Ahmed Shamil
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.v13i2.5582

Abstract

Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.
MyPharmaceutical: an interactive proof of concept Jie, Khor Ying; Zaaba, Zarul Fitri; Omar, Mohd Adib
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.v13i3.5896

Abstract

With the rise of health awareness, pharmaceutical and cosmetic products should be verified to protect ourselves from health risks. MyPharmaceutical is a proof-of-concept proposed to provide a mobile application for users to carry out product verification and reporting and a web application for administrative purposes. The data on the registered pharmaceutical and cosmetic products were extracted from national pharmaceutical regulatory agency (NPRA) website. MyPharmaceutical mobile application provides functionalities such as searching the registered product, bookmarking products, reporting products, and tracking report status. The mobile application also implemented a barcode scanner feature to provide ease of product verification. A named entity recognition algorithm is applied with the NLP.js library to provide an improved product search feature for the users, where products can be searched with multiple search criteria in a single input. The web application is proposed to support the mobile application, where the NPRA data admins and officers can manage reported products, publish announcements, verify product data, and utilize the analytic dashboard. The system proposed is expected to provide ease of product verification and reporting to assist the public in choosing safe registered products and a platform for NPRA to manage data and deliver information to the users.
Application of smart hydrogels scaffolds for bone tissue engineering Owida, Hamza Abu; Alnaimat, Feras; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad; Turab, Nidal M.
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.v13i6.7608

Abstract

Recent attention in the biomedical and orthopedic sectors has been drawn towards bone defects, emerging as a prominent focus within orthopedic clinics. Hydrogels, due to their biocompatibility, elevated water content, softness, and flexibility, are increasingly acknowledged in tissue regeneration research. Advanced biomaterials offer numerous advantages over traditional materials, notably the capacity to respond to diverse physical, chemical, and biological stimuli. Their responsiveness to environmental cues, such as three-dimensional (3D) morphology and phase conditions, holds promise for enhancing the efficacy of localized bone lesion repairs. This paper aims to revolutionize the treatment of severe bone abnormalities by providing a comprehensive examination of hydrogels capable of morphological adaptation to environmental changes. It delineates their classification, manufacturing principles, and current research status within the field of bone defect regeneration.
The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization Leong, Jing Mei; Ahmad Hijazi, Mohd Hanafi; Saudi, Azali; Kim On, Chin; Fui Fui, Ching; Haviluddin, Haviluddin
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.v13i2.5840

Abstract

The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.
Comparative analysis of reactive routing protocols for vehicular adhoc network communications Kaushal, Payal; Khurana, Meenu; Ramkumar, Ketti Ramchandran
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.v13i3.5322

Abstract

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.
Analysis of multi-criteria recommendation system based on fuzzy algorithm Anaam, Elham Abdulwahab; Haw, Su-Cheng; Ng, Kok-Why; Naveen, Palanichamy; Tong, Gee-Kok
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.v13i4.7801

Abstract

There is a gap in defining the multi-criteria decision-making issues and with recommendation techniques and theories that can help develop the modulation coefficient recommenders. The main objective of this research is to identify an in-depth examination of the category of multiple variables recommendation systems. The methodology that is used in the current study is fuzzy multi-critical decision-making to enhance the precision and appropriateness of the recommendations provided to users, and make recommendations by representing an individual's performance for the product as an ordered collection of rankings in addition to different parameters. The techniques used to make forecasts and produce recommendations using multi-criteria rankings are reviewed. In addition, we propose the multiple-criteria ranking algorithms. Experimental evaluations demonstrated that our proposed algorithms can solve the multi-criteria issues. Furthermore, the research considers unresolved problems and upcoming difficulties for the category of recommendations for multiple variables ratings.
A novel method of detecting malware on Android mobile devices with explainable artificial intelligence Vanjire, Seema Sachin; Lakshmi, Mohandoss
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.v13i3.6986

Abstract

The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique.
Optimization of a CH3NH3SnI3 based lead-free organic perovskite solar cell using SCAPS-1D simulator Rana, Md. Sohel; Abdur Razzak, Md.
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.v13i4.7333

Abstract

In this study, a CH3NH3SnI3-based perovskite PV cell with the structure (FTO/TiO2/CH3NH3SnI3/Cu2O) was made and optimized by changing the layer thickness, defect density, and doping profile using the solar cell capacitance simulator (SCAPS) 1D simulator. To better understand how the device interface affects carrier dynamics, a synergic optimization of the device is done by altering the electron-transport layer (ETL) and hole-transport layer (HTL) materials. The light glows through the window layer of Sn2O: F, which serves as the transparent conducting oxide layer in our suggested cell construction and then travels over TiO2 as an n-type ETL. Due to its unique features, the p-type perovskite (CH3NH3SnI3) is chosen as the primary absorber layer. Lastly, Cu2O is added as an HTL before the back contact because it has a higher hole conductivity and the proper offsets for spreading the valance and conduction bands. Additionally, Cu2O-based devices outperform frequently utilized spiro-OMeTAD-based devices in terms of efficiency. According to the findings of these simulations, the optimized structure has a power conversion efficiency (PCE) of 41%, an open-circuit voltage of 1.32 V, a short-circuit current density of 34.31 mA/cm2 and a fill factor (FF) of 90.5%. Additionally, the optimized structure has a short-circuit current density of 34.31 mA/cm2.
An optimization based deep learning approach for human activity recognition in healthcare monitoring Kalyanasundaram, Aparna; Panathula, Ganesh
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.v13i6.8000

Abstract

Medical images are comprised of sensor measurements which help detect the characteristics of diseases. Computer-based analysis results in the early detection of diseases and suitable medications. Human activity recognition (HAR) is highly useful in applications related to medical care, fitness tracking, and patient data archiving. There are two kinds of data fed into the HAR system which are, image data and time series data of physical movements through accelerometers and gyroscopes present in smart devices. This study introduced crayfish optimization algorithm with long short term memory (COA-LSTM). The raw data is obtained from three datasets namely, WISDM, UCI-HAR, and PAMAP2 datasets; then, pre-processing helps in removal of unwanted information. The features from pre-processed data are reduced using principal component analysis and linear discriminant analysis (PCA-LDA). Finally, classification is performed using COA-LSTM where, the hyperparameters are fine-tuned with the help of COA. The suggested method achieves a classification accuracy of 98.23% for UCI-HAR dataset, whereas the existing techniques like convolutional neural network (CNN), multi-branch CNN-bidirectional LSTM, CNN with gated recurrent unit (GRU), ST-deep HAR, and Ensem-HAR obtain a classification accuracy of 91.98%, 96.37%, 96.20%, 97.7%, and 95.05%, respectively.

Page 241 of 291 | Total Record : 2901

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