Bulletin of Electrical Engineering and Informatics, 2024
The public cloud environment has emerged as a promising platform for executing scientific workflo... more The public cloud environment has emerged as a promising platform for executing scientific workflows. These executions involve leasing virtual machines (VMs) from public services for the duration of the workflow. The structure of the workflows significantly impacts the performance of any proposed scheduling approach. A task within a workflow cannot begin its execution before receiving all the required data from its preceding tasks. In this paper, we introduce a multi-priority scheduling approach for executing workflow tasks in the cloud. The key component of the proposed approach is a mechanism that logically orders and groups workflow tasks based on their data dependencies and locality. Using the proposed approach, the number of available VMs influences the number of groups (partitions) obtained. Based on the locality of each group’s tasks, the priority of each group is determined to reduce the overall execution delay and improve VM utilization. As the results demonstrate, the proposed approach achieves a significant reduction in both execution costs and time in most scenarios.
Bulletin of Electrical Engineering and Informatics, 2024
There is a gap in defining the multi-criteria decision-making issues and with recommendation tech... more 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.
Bulletin of Electrical Engineering and Informatics, 2024
In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems w... more In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.
Bulletin of Electrical Engineering and Informatics, 2024
Artificial intelligence (AI) is one of the most common and essential technologies in this modern ... more Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
Bulletin of Electrical Engineering and Informatics, 2024
Several voltage stability indices (VSIs) have been developed to assess the potential for voltage ... more Several voltage stability indices (VSIs) have been developed to assess the potential for voltage collapse. However, certain indexes are computationally costly. Meanwhile, some have been noted to underperform across various conditions. This work proposes a novel line index called the super voltage stability index (SVSI) to calculate the system's voltage stability margin (VSM). The suggested approach is based on the transmission system's two bus systems. The reactive power loss and N-1 contingency conditions to voltage sensitivity is a unique calculation approach used in this study to identify voltage instability. Day to day, the demand for electric power is being increased due to incessant increments in technology and population growth. Therefore, the power system networks are under pressure. The operational conditions of transmission system networks are affected at this point, which may result in voltage collapse. Regular monitoring of power supply is essential to avert voltage collapse. The effectiveness of the suggested index has been assessed using the IEEE 5 and 30-bus systems across diverse operating scenarios, including variations in active and reactive power loading as well as single line losses. The findings indicate that SVSI provides a more reliable indication of the proximity to voltage collapse when compared to conventional line VSIs.
Bulletin of Electrical Engineering and Informatics, 2024
This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enh... more This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enhancing performance and grid integration through a comprehensive simulation approach. By employing advanced simulation tools in Simulink ® and MATLAB ® , alongside electrical installation planning with SIMARIS ® , we meticulously analyze the charging process, infrastructure requirements, and their implications on the power grid. Our results demonstrate significant improvements in charging station efficiency and reliability, highlighting the effectiveness of our proposed control strategies and harmonic mitigation techniques. Notably, the integration of renewable energy sources emerges as a pivotal factor in reducing operational costs and carbon emissions, furthering the sustainability of EV charging solutions. The research delineates the environmental benefits, emphasizing the reduction of greenhouse gas emissions and enhancement of urban air quality, pivotal in the global shift towards cleaner transportation modes. This work contributes valuable insights into the design and grid integration of EV charging stations, offering a scalable model for future infrastructure development. It serves as a critical resource for engineers, policymakers, and stakeholders in the realm of electric mobility, advocating for a strategic transition to EVs supported by robust and efficient charging infrastructure. This is an open access article under the CC BY-SA license.
Bulletin of Electrical Engineering and Informatics, 2024
There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infe... more There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections will continue to rise, and there is now no safe and effective vaccination available to prevent a pandemic. This has increased the need for rapid, sensitive, and highly selective diagnostic techniques for coronavirus disease (COVID-19) detection to levels never seen before. Researchers are now looking at other biosensing techniques that may be able to detect the COVID-19 infection and stop its spread. According to high sensitivity, and selectivity that could provide real-time results at a reasonable cost, nanomaterial show great promise for quick coronavirus detection. In order to better comprehend the rapid course of the infection and administer more effective treatments, these diagnostic methods can be used for widespread COVID-19 identification. This article summarises the current state of research into nanomaterial-based biosensors for quick SARS-CoV-2 diagnosis as well as the prospects for future advancement in this field. This research will be very useful during the COVID-19 epidemic in terms of establishing rules for designing nanostructure materials to deal with the outbreak. In order to predict the spread of the SARS-CoV-2 virus, we investigate the advantages of using nano-structure material and its biosensing applications.
Bulletin of Electrical Engineering and Informatics, 2024
The increasing use of the internet for health information brings challenges due to the complexity... more The increasing use of the internet for health information brings challenges due to the complexity and abundance of data, leading to information overload. This highlights the necessity of implementing recommender systems (RSs) within the healthcare domain, with the aim of facilitating more effective and precise healthcare-related decisions for both healthcare providers and users. Health recommendation systems can suggest suitable healthcare items or services based on users' health conditions and needs, including medications, diagnoses, hospitals, doctors, and healthcare services. Despite their potential benefits, RSs encounter significant limitations, including data sparsity, which can lead to recommendations that are unreliable and misleading. Considering the increasing significance of health recommendation systems and the challenge of sparse data, we propose an effective approach to improve precision and coverage in recommending healthcare items or services. This aims to assist users and healthcare practitioners in making informed decisions tailored to their unique needs and health conditions. Empirical testing on two healthcare rating datasets, including sparse datasets, illustrate that our proposed approach outperforms baseline recommendation methods. It excels in improving both the precision and coverage of health-related recommendations, demonstrating effective handling of extremely sparse datasets.
Bulletin of Electrical Engineering and Informatics, 2024
Teaching and learning for autistic students during the COVID-19 pandemic pose challenges for educ... more Teaching and learning for autistic students during the COVID-19 pandemic pose challenges for educators. This systematic literature review (SLR) aimed to explore the effectiveness of virtual teaching and learning (VTL) by employing the reporting standards for systematic evidence syntheses (ROSES) framework. Articles from databases like Scopus, Web of Science, and Google Scholar were systematically examined, focusing on themes such as support, coping strategies, teaching methods, flexibility, and communication. The review identified 14 sub-themes within these categories, providing tailored coping and teaching strategies for parents, teachers, and caregivers working with autistic students. From 706 initially identified articles, 376 were selected, with 17 specifically relevant to virtual teaching for autistic students during the pandemic. These findings contribute insights to the existing literature and offer practical implications to enhance VTL experiences for autistic students facing pandemic challenges.
Bulletin of Electrical Engineering and Informatics, 2024
Vendor selection techniques are very important to maintain supply chain services, optimal service... more Vendor selection techniques are very important to maintain supply chain services, optimal service creates strong consistency in maintaining the continuity of supply chain business processes. The aim of this research is to provide an objective and consistent understanding of the best techniques in vendor selection which are implemented openly through the collaboration of multi-criteria decision making-analytic hierarchy process (MCDM-AHP) and ELECTRE. Empirical studies show how this approach is able to provide optimal decision-making support for the vendor selection process. Eight criteria are required which have contradictory meanings in their apps. These criteria include quality of goods (QG), payment methods (PMs), payment terms (PTs), minimum transactions (MTs), discounts (DS), delivery times (DTs), inventory (IN), and service (SV). The comparison importance value of the criteria is used as a measure of weighting the criteria through two testing approaches, namely mathematical algebra matrices and expert choice apps, through accurately assessing the optimal eigenvector from the two test approaches. Decision making support was carried out by comparison using 342 preference matrices which were developed into concordance and discordance matrices, the elimination process with threshold matrices found that the ranking results of four vendors were ranked first as worthy of being a selection priority and fifteen other vendors were ranked below.
Bulletin of Electrical Engineering and Informatics, 2024
Flowers have a variety of shapes, colors and structures, the images of which need to be classifie... more Flowers have a variety of shapes, colors and structures, the images of which need to be classified using guided learning techniques. Several studies classify flowers using machine learning, but their accuracy performance is not good. The thing is, the flowers come in a variety of colors that can sometimes look similar to the background. Therefore, this study aims to classify flowers using a convolutional neural network (CNN) and measure its performance. The method used is mixed methods by collecting existing data from previous studies and connecting it with the realities in the field. The Kozłowski and Steinbrener models were used, while the image data was obtained from the Oxford17 and Oxford102 dataset with 17 and 102 flower types, respectively. The results show 60% and 84% accuracy of CNN using the scratch and transfer learning approach for the Oxford17 dataset. The Oxford102 dataset shows 42% and 64%, respectively, with CNN from baseline and transfer learning.
Bulletin of Electrical Engineering and Informatics, 2024
Several enterprises implemented enterprise architecture (EA) projects to align business and infor... more Several enterprises implemented enterprise architecture (EA) projects to align business and information technology (IT) strategies. The evaluation process is needed to ensure the implementation of EA projects provides effectiveness, efficiency, and feasibility of EA information systems (IS) and assesses previous project experience to avoid future EA project risks. The study aims to present a systematic literature review (SLR) of the models and evaluation methods used or developed, especially in the field of EA research. Based on the inclusion and exclusion criteria, 21 articles were selected for review. The results of the study present an overview of the models and methods used as well as new approaches developed for EA evaluation as well as information based on approaches related to models and methods identified as organizing information and data analysis to broaden future research insights. The literature review also provides additional simple theories related to the implications and techniques of the identified models and methods. The study contributes to company stakeholders to encourage the implementation of EA, identify improvements and enhancements to EA projects as well as further references and insights for practitioners and researchers regarding EA evaluation as an effort to assess the success of achieving enterprise goals.
Bulletin of Electrical Engineering and Informatics, 2024
In recent years, there has been increased interest in using digital health tools to improve healt... more In recent years, there has been increased interest in using digital health tools to improve healthcare outcomes and encourage healthy lifestyles. However, older adults, who often face challenges with technology, may encounter difficulties when using these tools. This study focused on understanding how elderly individuals experience the MySejahtera app, a digital health tool designed to help manage health during a pandemic. The research involved 30 elderly users through focus groups, interviews, and user experience evaluations. The study identified key themes in the elderly user experience, such as the need for simplicity, efficiency in the app's interface, ease of learning, concerns about security, and emotional reactions. Recommendations were made to enhance the engagement of elderly users with digital health apps. While the MySejahtera app shows promise for older adults, the study highlights the importance of addressing specific design considerations and providing support to improve user satisfaction. Overall, the research offers valuable insights and recommendations for designing and implementing digital health applications that better meet the needs and preferences of elderly users.
Bulletin of Electrical Engineering and Informatics, 2024
In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in... more In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.
Bulletin of Electrical Engineering and Informatics, 2024
The 5G-enabled vehicular network is an innovative technology that has promise for intelligent tra... more The 5G-enabled vehicular network is an innovative technology that has promise for intelligent transportation systems. It enables the transmitting of messages about traffic that deliver the most recent information on congestion, road conditions, and driving surroundings. The communication channel used by vehicle networks is inherently open, which unfortunately exposes the system to privacy and security concerns. To solve the problems of deploying a safe vehicular network, some academics have put forth plans. However, a number of current methods have significant computational or communication overhead costs. To solve this problem, an efficient and secure authentication with a privacypreserving (ES-APP) scheme established elliptic curve encryption is introduced. With the proposed ES-APP, the data signed and verified for vehicle-to-vehicle and vehicle-to-infrastructure modes in the 5G-based vehicular network are more effective. The ES-APP scheme's goal is to meet the criteria for the security and privacy of the 5G-enabled automotive network. Ultimately, this work discusses the critical survey of the existing studies and the expected outcome for the ES-APP scheme and further works in the 5G-enabled vehicular network. This is an open access article under the CC BY-SA license.
Bulletin of Electrical Engineering and Informatics, 2024
Recent advancements in biosensors have empowered individuals with diabetes to autonomously monito... more Recent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3 sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3 sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision making by identifying and addressing extreme data points.
Bulletin of Electrical Engineering and Informatics, 2024
An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essent... more An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essential for applications related to global navigation satellite systems (GNSS) under varying weather conditions. The previous TEC prediction schemes contribute for each time step that increases the prediction time. The eye contact phenomenon establishes a metaphorical connection which intends to capture and emphasize the attention worthy elements in a sequence. This research introduces a deep learning approach which is a combination of attention-based bidirectional long short-term memory and gated recurrent unit (Bi-LSTM GRU) to predict TEC in the ionosphere. Bidirectional LSTM is the better option for achieving durability when combined with a gated recurrent unit (GRU) to predict TEC in the ionosphere. The proposed approach is evaluated with the existing LSTM approach for root mean square error (RMSE) during training and validation. The RMSE while predicting the global ionospheric delay using the existing LSTM for 20 epochs is seen to be 0.004, whereas the existing approach achieves a training error of 0.003.
Bulletin of Electrical Engineering and Informatics, 2024
Forest ecosystems play a crucial role in providing a wide range of ecological, social, and econom... more Forest ecosystems play a crucial role in providing a wide range of ecological, social, and economic benefits. However, the increasing frequency and severity of forest fires pose a significant threat to the sustainability of forests and their functions, highlighting the need for early detection and swift action to mitigate damage. The combination of drones and artificial intelligence, particularly deep learning, proves to be a cost-effective solution for accurately and efficiently detecting forest fires in real-time. Deep learning-based image segmentation models can not only be employed for forest fire detection but also play a vital role in damage assessment and support reforestation efforts. Furthermore, the integration of thermal cameras on drones can significantly enhance the sensitivity in forest fire detection. This study undertakes an in-depth analysis of recent advancements in deep learning-based semantic segmentation, with a particular focus on model’s mask region convolutional neural network (Mask R-CNN) and you only look once (YOLO) v5, v7, and v8 variants. Emphasis is placed on their suitability for forest fire monitoring using drones equipped with RGB and/or thermal cameras. The conducted experiments have yielded encouraging outcomes across various metrics, underscoring its significance as an invaluable asset for both fire detection and continuous monitoring endeavors.
Bulletin of Electrical Engineering and Informatics, 2024
Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected ... more Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected and is being shifted towards online shopping. Understanding the customers’ opinions, attitudes, and emotions in feedback and comments plays an essential role in making decisions for organizations and individuals (e.g., companies and customers). In this study, we propose sentiment summaries from the customer knowledgebase (SSoCK) framework that analyses customer feedback and improve a mechanism for sentiment summarization by using text analysis including sentiment analysis. In the experiments, various domains from customer reviews (e.g., computer and Canon) are used to conduct. The results show that the proposed SSoCK framework has the high performance of sentiment classification in terms of its accuracy when compared to the other approaches. Moreover, the proposed framework generates various kinds of sentiment summaries that can support managers/potential customers understand trending/interesting aspects of the product with customer satisfaction and can be easily updated with new reviews within the same domain without storing any original data.
Bulletin of Electrical Engineering and Informatics, 2024
Chatbots are important in artificial intelligence (AI) and natural language processing (NLP). The... more Chatbots are important in artificial intelligence (AI) and natural language processing (NLP). The development of the chatbot is viewed as a continuous issue in the field. This is suitable for Arabic chatbots that are not widely available. This study aims to fill the gap in Arabic chatbot development by creating an Arabic chatbot system for university admissions. The system uses a deep neural network model and a manually constructed dataset for conversation pairings, utilizing the Jordanian Arabic dialect from Al-Zaytoonah University of Jordan’s (ZUJ) website. The system efficiently answers most user queries, improving the counseling experience and reducing workload in the admissions department. The adoption of this system also minimizes website traffic congestion. The study contributes to the improvement of Arabic chatbot technology by creating a deep learning-based system optimized for university admissions, demonstrating its potential impact in the Arabic-speaking context. Future research can further enhance the system’s capabilities and its applicability in other disciplines.
Bulletin of Electrical Engineering and Informatics, 2024
The public cloud environment has emerged as a promising platform for executing scientific workflo... more The public cloud environment has emerged as a promising platform for executing scientific workflows. These executions involve leasing virtual machines (VMs) from public services for the duration of the workflow. The structure of the workflows significantly impacts the performance of any proposed scheduling approach. A task within a workflow cannot begin its execution before receiving all the required data from its preceding tasks. In this paper, we introduce a multi-priority scheduling approach for executing workflow tasks in the cloud. The key component of the proposed approach is a mechanism that logically orders and groups workflow tasks based on their data dependencies and locality. Using the proposed approach, the number of available VMs influences the number of groups (partitions) obtained. Based on the locality of each group’s tasks, the priority of each group is determined to reduce the overall execution delay and improve VM utilization. As the results demonstrate, the proposed approach achieves a significant reduction in both execution costs and time in most scenarios.
Bulletin of Electrical Engineering and Informatics, 2024
There is a gap in defining the multi-criteria decision-making issues and with recommendation tech... more 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.
Bulletin of Electrical Engineering and Informatics, 2024
In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems w... more In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.
Bulletin of Electrical Engineering and Informatics, 2024
Artificial intelligence (AI) is one of the most common and essential technologies in this modern ... more Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
Bulletin of Electrical Engineering and Informatics, 2024
Several voltage stability indices (VSIs) have been developed to assess the potential for voltage ... more Several voltage stability indices (VSIs) have been developed to assess the potential for voltage collapse. However, certain indexes are computationally costly. Meanwhile, some have been noted to underperform across various conditions. This work proposes a novel line index called the super voltage stability index (SVSI) to calculate the system's voltage stability margin (VSM). The suggested approach is based on the transmission system's two bus systems. The reactive power loss and N-1 contingency conditions to voltage sensitivity is a unique calculation approach used in this study to identify voltage instability. Day to day, the demand for electric power is being increased due to incessant increments in technology and population growth. Therefore, the power system networks are under pressure. The operational conditions of transmission system networks are affected at this point, which may result in voltage collapse. Regular monitoring of power supply is essential to avert voltage collapse. The effectiveness of the suggested index has been assessed using the IEEE 5 and 30-bus systems across diverse operating scenarios, including variations in active and reactive power loading as well as single line losses. The findings indicate that SVSI provides a more reliable indication of the proximity to voltage collapse when compared to conventional line VSIs.
Bulletin of Electrical Engineering and Informatics, 2024
This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enh... more This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enhancing performance and grid integration through a comprehensive simulation approach. By employing advanced simulation tools in Simulink ® and MATLAB ® , alongside electrical installation planning with SIMARIS ® , we meticulously analyze the charging process, infrastructure requirements, and their implications on the power grid. Our results demonstrate significant improvements in charging station efficiency and reliability, highlighting the effectiveness of our proposed control strategies and harmonic mitigation techniques. Notably, the integration of renewable energy sources emerges as a pivotal factor in reducing operational costs and carbon emissions, furthering the sustainability of EV charging solutions. The research delineates the environmental benefits, emphasizing the reduction of greenhouse gas emissions and enhancement of urban air quality, pivotal in the global shift towards cleaner transportation modes. This work contributes valuable insights into the design and grid integration of EV charging stations, offering a scalable model for future infrastructure development. It serves as a critical resource for engineers, policymakers, and stakeholders in the realm of electric mobility, advocating for a strategic transition to EVs supported by robust and efficient charging infrastructure. This is an open access article under the CC BY-SA license.
Bulletin of Electrical Engineering and Informatics, 2024
There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infe... more There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections will continue to rise, and there is now no safe and effective vaccination available to prevent a pandemic. This has increased the need for rapid, sensitive, and highly selective diagnostic techniques for coronavirus disease (COVID-19) detection to levels never seen before. Researchers are now looking at other biosensing techniques that may be able to detect the COVID-19 infection and stop its spread. According to high sensitivity, and selectivity that could provide real-time results at a reasonable cost, nanomaterial show great promise for quick coronavirus detection. In order to better comprehend the rapid course of the infection and administer more effective treatments, these diagnostic methods can be used for widespread COVID-19 identification. This article summarises the current state of research into nanomaterial-based biosensors for quick SARS-CoV-2 diagnosis as well as the prospects for future advancement in this field. This research will be very useful during the COVID-19 epidemic in terms of establishing rules for designing nanostructure materials to deal with the outbreak. In order to predict the spread of the SARS-CoV-2 virus, we investigate the advantages of using nano-structure material and its biosensing applications.
Bulletin of Electrical Engineering and Informatics, 2024
The increasing use of the internet for health information brings challenges due to the complexity... more The increasing use of the internet for health information brings challenges due to the complexity and abundance of data, leading to information overload. This highlights the necessity of implementing recommender systems (RSs) within the healthcare domain, with the aim of facilitating more effective and precise healthcare-related decisions for both healthcare providers and users. Health recommendation systems can suggest suitable healthcare items or services based on users' health conditions and needs, including medications, diagnoses, hospitals, doctors, and healthcare services. Despite their potential benefits, RSs encounter significant limitations, including data sparsity, which can lead to recommendations that are unreliable and misleading. Considering the increasing significance of health recommendation systems and the challenge of sparse data, we propose an effective approach to improve precision and coverage in recommending healthcare items or services. This aims to assist users and healthcare practitioners in making informed decisions tailored to their unique needs and health conditions. Empirical testing on two healthcare rating datasets, including sparse datasets, illustrate that our proposed approach outperforms baseline recommendation methods. It excels in improving both the precision and coverage of health-related recommendations, demonstrating effective handling of extremely sparse datasets.
Bulletin of Electrical Engineering and Informatics, 2024
Teaching and learning for autistic students during the COVID-19 pandemic pose challenges for educ... more Teaching and learning for autistic students during the COVID-19 pandemic pose challenges for educators. This systematic literature review (SLR) aimed to explore the effectiveness of virtual teaching and learning (VTL) by employing the reporting standards for systematic evidence syntheses (ROSES) framework. Articles from databases like Scopus, Web of Science, and Google Scholar were systematically examined, focusing on themes such as support, coping strategies, teaching methods, flexibility, and communication. The review identified 14 sub-themes within these categories, providing tailored coping and teaching strategies for parents, teachers, and caregivers working with autistic students. From 706 initially identified articles, 376 were selected, with 17 specifically relevant to virtual teaching for autistic students during the pandemic. These findings contribute insights to the existing literature and offer practical implications to enhance VTL experiences for autistic students facing pandemic challenges.
Bulletin of Electrical Engineering and Informatics, 2024
Vendor selection techniques are very important to maintain supply chain services, optimal service... more Vendor selection techniques are very important to maintain supply chain services, optimal service creates strong consistency in maintaining the continuity of supply chain business processes. The aim of this research is to provide an objective and consistent understanding of the best techniques in vendor selection which are implemented openly through the collaboration of multi-criteria decision making-analytic hierarchy process (MCDM-AHP) and ELECTRE. Empirical studies show how this approach is able to provide optimal decision-making support for the vendor selection process. Eight criteria are required which have contradictory meanings in their apps. These criteria include quality of goods (QG), payment methods (PMs), payment terms (PTs), minimum transactions (MTs), discounts (DS), delivery times (DTs), inventory (IN), and service (SV). The comparison importance value of the criteria is used as a measure of weighting the criteria through two testing approaches, namely mathematical algebra matrices and expert choice apps, through accurately assessing the optimal eigenvector from the two test approaches. Decision making support was carried out by comparison using 342 preference matrices which were developed into concordance and discordance matrices, the elimination process with threshold matrices found that the ranking results of four vendors were ranked first as worthy of being a selection priority and fifteen other vendors were ranked below.
Bulletin of Electrical Engineering and Informatics, 2024
Flowers have a variety of shapes, colors and structures, the images of which need to be classifie... more Flowers have a variety of shapes, colors and structures, the images of which need to be classified using guided learning techniques. Several studies classify flowers using machine learning, but their accuracy performance is not good. The thing is, the flowers come in a variety of colors that can sometimes look similar to the background. Therefore, this study aims to classify flowers using a convolutional neural network (CNN) and measure its performance. The method used is mixed methods by collecting existing data from previous studies and connecting it with the realities in the field. The Kozłowski and Steinbrener models were used, while the image data was obtained from the Oxford17 and Oxford102 dataset with 17 and 102 flower types, respectively. The results show 60% and 84% accuracy of CNN using the scratch and transfer learning approach for the Oxford17 dataset. The Oxford102 dataset shows 42% and 64%, respectively, with CNN from baseline and transfer learning.
Bulletin of Electrical Engineering and Informatics, 2024
Several enterprises implemented enterprise architecture (EA) projects to align business and infor... more Several enterprises implemented enterprise architecture (EA) projects to align business and information technology (IT) strategies. The evaluation process is needed to ensure the implementation of EA projects provides effectiveness, efficiency, and feasibility of EA information systems (IS) and assesses previous project experience to avoid future EA project risks. The study aims to present a systematic literature review (SLR) of the models and evaluation methods used or developed, especially in the field of EA research. Based on the inclusion and exclusion criteria, 21 articles were selected for review. The results of the study present an overview of the models and methods used as well as new approaches developed for EA evaluation as well as information based on approaches related to models and methods identified as organizing information and data analysis to broaden future research insights. The literature review also provides additional simple theories related to the implications and techniques of the identified models and methods. The study contributes to company stakeholders to encourage the implementation of EA, identify improvements and enhancements to EA projects as well as further references and insights for practitioners and researchers regarding EA evaluation as an effort to assess the success of achieving enterprise goals.
Bulletin of Electrical Engineering and Informatics, 2024
In recent years, there has been increased interest in using digital health tools to improve healt... more In recent years, there has been increased interest in using digital health tools to improve healthcare outcomes and encourage healthy lifestyles. However, older adults, who often face challenges with technology, may encounter difficulties when using these tools. This study focused on understanding how elderly individuals experience the MySejahtera app, a digital health tool designed to help manage health during a pandemic. The research involved 30 elderly users through focus groups, interviews, and user experience evaluations. The study identified key themes in the elderly user experience, such as the need for simplicity, efficiency in the app's interface, ease of learning, concerns about security, and emotional reactions. Recommendations were made to enhance the engagement of elderly users with digital health apps. While the MySejahtera app shows promise for older adults, the study highlights the importance of addressing specific design considerations and providing support to improve user satisfaction. Overall, the research offers valuable insights and recommendations for designing and implementing digital health applications that better meet the needs and preferences of elderly users.
Bulletin of Electrical Engineering and Informatics, 2024
In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in... more In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.
Bulletin of Electrical Engineering and Informatics, 2024
The 5G-enabled vehicular network is an innovative technology that has promise for intelligent tra... more The 5G-enabled vehicular network is an innovative technology that has promise for intelligent transportation systems. It enables the transmitting of messages about traffic that deliver the most recent information on congestion, road conditions, and driving surroundings. The communication channel used by vehicle networks is inherently open, which unfortunately exposes the system to privacy and security concerns. To solve the problems of deploying a safe vehicular network, some academics have put forth plans. However, a number of current methods have significant computational or communication overhead costs. To solve this problem, an efficient and secure authentication with a privacypreserving (ES-APP) scheme established elliptic curve encryption is introduced. With the proposed ES-APP, the data signed and verified for vehicle-to-vehicle and vehicle-to-infrastructure modes in the 5G-based vehicular network are more effective. The ES-APP scheme's goal is to meet the criteria for the security and privacy of the 5G-enabled automotive network. Ultimately, this work discusses the critical survey of the existing studies and the expected outcome for the ES-APP scheme and further works in the 5G-enabled vehicular network. This is an open access article under the CC BY-SA license.
Bulletin of Electrical Engineering and Informatics, 2024
Recent advancements in biosensors have empowered individuals with diabetes to autonomously monito... more Recent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3 sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3 sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision making by identifying and addressing extreme data points.
Bulletin of Electrical Engineering and Informatics, 2024
An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essent... more An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essential for applications related to global navigation satellite systems (GNSS) under varying weather conditions. The previous TEC prediction schemes contribute for each time step that increases the prediction time. The eye contact phenomenon establishes a metaphorical connection which intends to capture and emphasize the attention worthy elements in a sequence. This research introduces a deep learning approach which is a combination of attention-based bidirectional long short-term memory and gated recurrent unit (Bi-LSTM GRU) to predict TEC in the ionosphere. Bidirectional LSTM is the better option for achieving durability when combined with a gated recurrent unit (GRU) to predict TEC in the ionosphere. The proposed approach is evaluated with the existing LSTM approach for root mean square error (RMSE) during training and validation. The RMSE while predicting the global ionospheric delay using the existing LSTM for 20 epochs is seen to be 0.004, whereas the existing approach achieves a training error of 0.003.
Bulletin of Electrical Engineering and Informatics, 2024
Forest ecosystems play a crucial role in providing a wide range of ecological, social, and econom... more Forest ecosystems play a crucial role in providing a wide range of ecological, social, and economic benefits. However, the increasing frequency and severity of forest fires pose a significant threat to the sustainability of forests and their functions, highlighting the need for early detection and swift action to mitigate damage. The combination of drones and artificial intelligence, particularly deep learning, proves to be a cost-effective solution for accurately and efficiently detecting forest fires in real-time. Deep learning-based image segmentation models can not only be employed for forest fire detection but also play a vital role in damage assessment and support reforestation efforts. Furthermore, the integration of thermal cameras on drones can significantly enhance the sensitivity in forest fire detection. This study undertakes an in-depth analysis of recent advancements in deep learning-based semantic segmentation, with a particular focus on model’s mask region convolutional neural network (Mask R-CNN) and you only look once (YOLO) v5, v7, and v8 variants. Emphasis is placed on their suitability for forest fire monitoring using drones equipped with RGB and/or thermal cameras. The conducted experiments have yielded encouraging outcomes across various metrics, underscoring its significance as an invaluable asset for both fire detection and continuous monitoring endeavors.
Bulletin of Electrical Engineering and Informatics, 2024
Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected ... more Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected and is being shifted towards online shopping. Understanding the customers’ opinions, attitudes, and emotions in feedback and comments plays an essential role in making decisions for organizations and individuals (e.g., companies and customers). In this study, we propose sentiment summaries from the customer knowledgebase (SSoCK) framework that analyses customer feedback and improve a mechanism for sentiment summarization by using text analysis including sentiment analysis. In the experiments, various domains from customer reviews (e.g., computer and Canon) are used to conduct. The results show that the proposed SSoCK framework has the high performance of sentiment classification in terms of its accuracy when compared to the other approaches. Moreover, the proposed framework generates various kinds of sentiment summaries that can support managers/potential customers understand trending/interesting aspects of the product with customer satisfaction and can be easily updated with new reviews within the same domain without storing any original data.
Bulletin of Electrical Engineering and Informatics, 2024
Chatbots are important in artificial intelligence (AI) and natural language processing (NLP). The... more Chatbots are important in artificial intelligence (AI) and natural language processing (NLP). The development of the chatbot is viewed as a continuous issue in the field. This is suitable for Arabic chatbots that are not widely available. This study aims to fill the gap in Arabic chatbot development by creating an Arabic chatbot system for university admissions. The system uses a deep neural network model and a manually constructed dataset for conversation pairings, utilizing the Jordanian Arabic dialect from Al-Zaytoonah University of Jordan’s (ZUJ) website. The system efficiently answers most user queries, improving the counseling experience and reducing workload in the admissions department. The adoption of this system also minimizes website traffic congestion. The study contributes to the improvement of Arabic chatbot technology by creating a deep learning-based system optimized for university admissions, demonstrating its potential impact in the Arabic-speaking context. Future research can further enhance the system’s capabilities and its applicability in other disciplines.
Uploads
Papers by beei iaes