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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.
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Articles 75 Documents
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Search results for , issue "Vol 13, No 5: October 2024" : 75 Documents clear
Comparative analysis of machine learning approaches in Kazakh banknote classification Sadyk, Ualikhan; Yerzhan, Makhambet; Baimukashev, Rashid; Turan, Cemil
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.v13i5.8004

Abstract

Nowadays, smartphones seamlessly blend into every aspect of our lives, including as handheld assistants for individuals with disabilities. Therefore, this research addresses the need for a robust system that can classify Kazakh banknotes. By capitalizing on the availability of smartphones and the ability to integrate detectors with classifiers this study introduces classifiers of Kazakh banknote images specifically designed for banknotes ranging from 500 KZT to 20,000 KZT. It compares traditional and hybrid machine learning (ML) approaches, utilizing a dataset of diverse banknote images, aiming for both lightweight and high accuracy. Competitive performance is demonstrated by the traditional approach, enhanced by thoughtful feature engineering. The hybrid approach, utilizing features from a pre-trained ResNet-18 model, showcases remarkable accuracy and robustness. Evaluation metrics reveal significant achievements, with the traditional approach attaining 94.00% accuracy and the hybrid approach excelling at 99.11%. Model stacking, combining classifiers from both approaches, outperforms individual classifiers, achieving 95.00% and 99.55% accuracy for the traditional and hybrid ML approaches, respectively. Our methodology’s comparable outcome in classifying Thai banknotes and coffee beans roasting levels demonstrates their versatility in image classification tasks that rely on color differentiation, showcasing the potential beyond banknote recognition.
FiMoDeAL: pilot study on shortest path heuristics in wireless sensor network for fire detection and alert ensemble Ifeanyi Akazue, Maureen; Efetobore Edje, Abel; Okpor, Margaret Dumebi; Adigwe, Wilfred; Ejeh, Patrick Ogholuwarami; Odiakaose, Christopher Chukwufunaya; Ojugo, Arnold Adimabua; Edim, Edim Bassey; Ako, Rita Erhovwo; Geteloma, Victor Ochuko
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.v13i5.8084

Abstract

With the incessant outbreak of fire, the heavy loss to both lives and properties in the society fire has since become a critical issue and challenge that needs our daily attention to be resolved. Loss of lives and properties to fire outbreak in 2021 alone as occurring in major Nigerian markets and residential homes was estimated at over 3 trillion Naira. Our study proposes a wireless sensor network internet of things (IoT) based ensemble to aid the effective monitoring, detection and alerting of residents and fire service departments. With cost as a major issue and the requisite installation of fire and smoke detectors in many houses our ensemble can efficiently integrate into the existing system using the ESP8285-controller to create a comprehensive access control system. The system provides real time monitor and control capabilities that will allow administrators to track and manage fire monitor and detection within a facility. Thus, enhances system's efficiency and performance.
Evaluation of steady-state ground resistance by field measurement and CDEGS computation Muhammad, Usman; Zaid, Hadee; Ahmad, Nurul Nadia; Mohamad Nor, Normiza; Aman, Fazlul
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.v13i5.7603

Abstract

In addition to the soil resistivity and size of the grounding system, grounding system configuration can influence the steady-state resistance (RDC) of a grounding system. The RDC of four to six configurations in three distinct soil conditions (sites 1 to 3) is measured using the fall-of-potential method and computed using the current distribution, electromagnetic fields, grounding, and soil structure analysis (CDEGS) simulation. The RDC value generally decreases as size increases, i.e., when more rods or tapes are added, except for a little variation subject to the electrode arrangement and soil resistivity. The 3 and 4-parallel configurations perform better on low resistivity soil (site 1), while the grid configurations (2×2- and 3-rod grids) are better on high resistivity soil (site 2). The difference between the measured and computed values at high soil resistivity sites (sites 2 and 3) is large, ranging from 18% to 66% for site 2 and from 35% to 53% for site 3. The difference is lower and more consistent at site 1, where five out of six configurations achieve less than 10%. At all sites, the difference between computed and measured RDCs generally decreases as the area of the electrode increases, except for some cases at site 2.
Genetic programming in machine learning based on the evaluation of house affordability classification Masrom, Suraya; Baharun, Norhayati; Mohamad Razi, Nor Faezah; Abd Rahman, Abdullah Sani; Mohammad, Nor Hazlina; Sarkam, Nor Aslily
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.v13i5.7594

Abstract

One of the big challenges in machine learning is difficulty of achieving high accuracy in a short completion time. A more difficulties appeared when the algorithm needs to be used for solving real dataset from the survey-based data collection. Imbalance dataset, insufficient strength of correlations, and outliers are common problems in real dataset. To accelerate the modelling processes, automated machine learning based on meta-heuristics optimization such as genetic programming (GP) has started to emerge and is gaining popularity. However, identifying the best hyper-parameters of the meta-heuristics’ algorithm is the critical issue. This paper demonstrates the evaluation of GP hyper-parameters in modeling machine learning on house affordability dataset. The important hyper-parameters of GP are population size (PS), that has been observed with different setting in this research. The machine learning with GP was used to predict house affordability among employers with transport expenditure and job mobility as some of the attributes. The results from testing that run on hold-out samples show that GP machine learning can reach to 70% accuracy with split ratio 0.2 and GP PS 30. This research contributes to the advancement of automated machine learning techniques, offering potential for faster and more accurate real survey-based datasets.
Image classification of fabric defects using ResNet50 deep transfer learning in FastAI Sitompul, Erwin; Leonhart Setiawan, Vincent; Jaya Tarigan, Hendra; Galina, Mia
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.v13i5.8218

Abstract

One of the most common issues in manufacturing is the inability to persistently maintain good quality, which can lead to product defects and customer complaints. In this research, the novel implementation of deep learning for fabric defect classification in FastAI was proposed. The residual network structure of ResNet50 was trained through transfer learning to classify the data set that contained five classes of fabric images: good, burned, frayed, ripped, and stained. A novel approach to constructing the data set was undertaken by compiling randomly downloaded fabric images within the aforementioned five classes with a broad variety from the internet. The effect of the two splitting methods in dividing the data into training and validation data was investigated. Random splitting divides the data into random class proportions, while stratified splitting maintains the original class proportions. Models were tested offline with unseen data and reached a mean accuracy of 92.5% for the 2-class model and 70.3% for the 5-class model. Based on the attained accuracy and precision, no splitting method was superior to the other. The feasibility of the system’s online implementation was evaluated by integrating a smartphone camera to capture and classify fabric samples, with a mean accuracy of 75.6% for the 5-class model.
Energy consumption forecasting: a case study on Bhashan Char island in Bangladesh Sarker, Md. Tanjil; Ramasamy, Gobbi; Al Farid, Fahmid; Mansor, Sarina; Abdul Karim, Hezerul
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.v13i5.7561

Abstract

In Bangladesh's distant regions, where dependable access to energy supplies is still an issue, effective energy consumption forecasting is essential for tackling the country's energy problems. In order to anticipate energy consumption in these neglected areas effectively, this study suggests a novel method that combines inverse matrix method (IMM) with linear regression method (LRM). The model produces accurate estimates by using historical data on energy use and relevant factors, such as weather patterns, population dynamics, economic indicators, and seasonal trends. A case study focusing on distant areas in Bangladesh shows how the proposed technique might be applied. The outcomes indicate how well the method captures the complex patterns of energy demand and how it may be used to guide sustainable energy management plans in these outlying regions. This study advances energy planning and resource allocation in areas with a limited supply of energy, paving the way for increased development and efficiency of the energy sector. Any rural or remote area in the globe can use these strategies to predict their short-term power consumption.
A comparative analysis of activation functions in neural networks: unveiling categories Bouraya, Sara; Belangour, Abdessamad
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.v13i5.7274

Abstract

Activation functions (AFs) play a critical role in artificial neural networks, allowing for the modeling of complex, non-linear relationships in data. In this review paper, we provide an overview of the most commonly used AFs in deep learning. In this comparative study, we survey and compare the different AFs in deep learning and artificial neural networks. Our aim is to provide insights into the strengths and weaknesses of each AF and to provide guidance on the appropriate selection of AFs for different types of problems. We evaluate the most commonly used AFs, including sigmoid, tanh, rectified linear units (ReLUs) and its variants, exponential linear unit (ELU), and SoftMax. For each activation category, we discuss its properties, mathematical formulation (MF), and the benefits and drawbacks in terms of its ability to model complex, non-linear relationships in data. In conclusion, this comparative study provides a comprehensive overview of the properties and performance of different AFs, and serves as a valuable resource for researchers and practitioners in deep learning and artificial neural networks.
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network Cahyani, Denis Eka; Hariadi, Anjar Dwi; Setyawan, Faisal Farris; Gumilar, Langlang; Setumin, Samsul
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.v13i5.7825

Abstract

Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies.
Improving cultural awareness and trust towards m-banking apps in Jordan Almuhairat, Ahmed; Alti, Adel; Alswailim, Mohannad
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.v13i5.7824

Abstract

Today’s mobile technology is improving our quality of life and changing our lifestyles by providing mobile financial applications that allow us to conduct daily financial transactions anytime and anywhere. As the number of mobile applications increases, great customer training offers innovative solutions suited to different customers’ cultures. Although Jordanian society has limited use of mobile banking applications due to a weak level of cultural awareness among consumers and financial security risks. Hence, we propose a culture-aware trust-based assistant to facilitate mobile banking transactions. It leverages the potential of guidance and behavior controllers to enhance awareness about available banking services while increasing confidence levels between Jordanians users for mobile apps. Particularly, an effective monitoring strategy and customer behavior controller aims to reduce fraud in mobile banking apps. An 18-user empirical study confirms that the completeness of financial culture and trust impact the customer’s attention to mobile banking apps. Therefore, the proposed assistant reaching an average interaction time of 20 seconds while achieving a high confidence rate of 74.05% which validates it is efficacy and practicability.
Controlling mobile robot in flat environment taking into account nonlinear factors applying artificial intelligence Huong, Tran Thi; Ha, Pham Thi Thu
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.v13i5.7818

Abstract

The article shows how to build and identify intelligent automatic control problems for mobile robots in a flat surface environment at the workplace, with known and unknown obstacles. Research and develop programming and control methods as an operating system for mobile robots robot operating system (ROS). Update map data information, in the operating environment, robot position control process, obstacle overcoming process simultaneous positioning and mapping (SLAM). From there, we aim to calculate and determine the robot's motion trajectory to get a smart path. The positioning trajectory calculation system robots. The authors use actor-critic (AC) algorithm to research and develop control. Research results in simulations, in Gazebo environment and test runs on real mobile robots have shown high-quality practical performance of automatic navigation and control while using this algorithm.

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