Journal of Electrical Systems and Information Technology最新文献

{"title":"A hybrid deep learning technique for spoofing website URL detection in real-time applications","authors":"Bridget C. Ujah-Ogbuagu, Oluwatobi Noah Akande, Emeka Ogbuju","doi":"10.1186/s43067-023-00128-8","DOIUrl":"https://doi.org/10.1186/s43067-023-00128-8","url":null,"abstract":"Website Uniform Resource Locator (URL) spoofing remains one of the ways of perpetrating phishing attacks in the twenty-first century. Hackers continue to employ URL spoofing to deceive naïve and unsuspecting consumers into releasing important personal details in malicious websites. Blacklists and rule-based filters that were once effective at reducing the risks and sophistication of phishing are no longer effective as there are over 1.5 million new phishing websites created monthly. Therefore, research aimed at unveiling new techniques for detecting phishing websites has sparked a lot of interest in both academics and business with machine and deep learning techniques being at the forefront. Among the deep learning techniques that have been employed, Convolutional Neural Network (CNN) remains one of the most widely used with high performance in feature learning. However, CNN has a problem of memorizing contextual relationships in URL text, which makes it challenging to efficiently detect sophisticated malicious URLs in real-time applications. On the contrary, Long Short-Term Memory (LSTM) deep learning model has been successfully employed in complex real-time problems because of its ability to store inputs for a long period of time. This study experiments with the use of hybrid CNN and LSTM deep learning models for spoofing website URL detection in order to exploit the combined strengths of the two approaches for a more sophisticated spoofing URL detection. Two publicly available datasets (UCL spoofing Website and PhishTank Datasets) were used to evaluate the performance of the proposed hybrid model against other models in the literature. The hybrid CNN-LSTM model achieved accuracies of 98.9% and 96.8%, respectively, when evaluated using the UCL and PhishTank datasets. On the other hand, the standalone CNN and LSTM achieved accuracies of 90.4% and 94.6% on the UCL dataset, while their accuracies on the PhishTank dataset were 89.3% and 92.6%, respectively. The results show that the hybrid CNN-LSTM algorithm largely outperformed the standalone CNN and LSTM models, which demonstrates a much better performance. Therefore, the hybrid deep learning technique is recommended for detecting spoofing website URL thereby reducing losses attributed to such attacks.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of resonant coupled inductor in a wireless power transfer system","authors":"Alan P. Nebrida","doi":"10.1186/s43067-024-00134-4","DOIUrl":"https://doi.org/10.1186/s43067-024-00134-4","url":null,"abstract":"Wireless power transfer (WPT) has garnered significant interest as a potentially transformative technology in the energy sector, as it presents a novel approach to powering and charging devices. The functionality of this technology is predicated upon the utilization of electromagnetic coupling to facilitate the wireless transmission of energy between two entities. Despite the considerable potential, wireless power transfer (WPT) faces significant obstacles that restrict its practical feasibility. One notable challenge that arises is the decrease in power transfer efficiency as the distance between the transmitter and receiver increases. Moreover, the wireless power transfer (WPT) technology is further limited by its reliance on accurate alignment between the transmitting source and the receiving device, thereby posing challenges for its practical implementation. The issues present substantial obstacles to the widespread commercialization of wireless power transfer (WPT). This study seeks to improve the efficacy of power transfer by optimizing the resonance frequency of the power transfer in response to the challenges. By systematically manipulating various parameters including coil dimensions, input voltage levels, and operational frequency, a novel approach is proposed to enhance the efficiency of power transfer. The study additionally offers valuable insights regarding the correlation between the distance separating the coils and the efficiency of power transfer. The findings of this study offer a thorough empirical analysis and are supported by a strong theoretical framework, resulting in a substantial coefficient of determination (R2 = 0.937118). This finding suggests that the linear regression model under consideration could account for approximately 93.7118 percent of the variability observed in the distance. The findings of this study establish a pathway toward enhanced and feasible wireless power technology, thereby establishing a robust basis for the prospective commercial implementation of wireless power transfer (WPT) systems.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"211 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inexpensive short-circuit current limiter and switching device based on one commutation circuit for a three-phase system","authors":"A. R. Ahmed, Abdallah M. Hamada, Saady Hasan, K. M. Abdel-Latif","doi":"10.1186/s43067-023-00131-z","DOIUrl":"https://doi.org/10.1186/s43067-023-00131-z","url":null,"abstract":"The escalating levels of fault currents resulting from short circuits, particularly in the context of distribution generators, have presented a critical need for the widespread implementation of fault current limiters (FCLs) in power systems. Despite their evident advantages, the extensive adoption of FCLs has been hindered by the high production costs associated with these devices. To address this challenge, a comprehensive study was conducted to develop a cost-effective FCL tailored specifically for three-phase power systems. This paper proposes a novel approach based on a single commutation circuit for the FCL and offers detailed insights into the construction of the FCL circuit, with a particular focus on efficient current interruption. Additionally, the study comprehensively discusses the logic controller and measurement system employed in conjunction with the proposed FCL, ensuring precise fault detection and rapid response to disturbances within the power grid. The integration of an artificial zero-crossing circuit within the FCL design further enhances its capability to limit short-circuit currents proactively, even before the occurrence of the first peak, thereby bolstering overall system reliability and stability. The study's significant contribution lies in achieving cost-effectiveness through the simplicity of the FCL's design, eliminating the need for extensive upgrades to various network components.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Financial technology decision support systems","authors":"Alper Pahsa","doi":"10.1186/s43067-023-00130-0","DOIUrl":"https://doi.org/10.1186/s43067-023-00130-0","url":null,"abstract":"The financial technology industry was the first and most successful to combine machine learning algorithms based on big data with artificial intelligence techniques. The use of decision support systems (DSSs) procedures will deliver financial products, service channels, service methodologies, and risk management in the quickest and most cost-efficient manners. The deep service value chain’s high-end finance was significantly impacted by the inventive and quick development of smart artificial intelligence (AI) and machine learning (ML) techniques brought about by all of the decision-making processes. This study presents digital services, including DSS procedures utilized in financial service operations. The fundamentals of DSS approaches, as well as AI and ML, are then demonstrated, and an example application of a campaign management system for tax repayment rescheduling is provided.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of COVID-19 patients from HRCT score prediction in CT images using transfer learning approach","authors":"Jitendra Tembhurne","doi":"10.1186/s43067-023-00129-7","DOIUrl":"https://doi.org/10.1186/s43067-023-00129-7","url":null,"abstract":"COVID-19 had a huge impact on patients and medical systems all around the world. Computed tomography (CT) images can effectively complement the reverse transcription-polymerase chain reaction testing (RT-PCR) and offer results much faster than RT-PCR test which assists to prevent spread of COVID-19. Various deep learning models have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help the diagnosis, but consisting of some benefits and limitations. Some of the reasons for this are: (i) training the data with largely unbalanced dataset and (ii) training the models with datasets having all similar CT images which leads to overfitting. In this work, we proposed a method to use multiple models to classify COVID-19 positive or negative which are trained using transfer learning techniques. In addition to classifying, if a person is COVID-19 positive or negative, we have also calculated the high-resolution computed tomography (HRCT) score or CT score to find the severity of infection with the help of image segmentation techniques, which assist in identifying the preliminary prognosis of the patient, and take necessary preventive measures.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139103061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing juice extraction in sugar mills: application of time delay compensation with intelligent controllers","authors":"Mohamed T. F. Saidahmed, Ahmed M. Attiya Ibrahim, Basma Gh. Elkilany","doi":"10.1186/s43067-023-00116-y","DOIUrl":"https://doi.org/10.1186/s43067-023-00116-y","url":null,"abstract":"The juice extraction process plays a crucial role in the sugar production industry, but it often faces challenges due to time delays that can negatively impact efficiency and quality. This paper introduces a Time Delay Compensation (TDC) technique aimed at mitigating this issue. The TDC technique is specifically designed to minimize the adverse effects of time delays, thereby improving control performance. The primary objective of this research is to enhance the efficiency and quality of the juice extraction process, ultimately leading to increased productivity and profitability for sugar mills. To achieve this goal, we conducted an extensive review of existing solutions, highlighting their limitations and associated challenges. In response, we propose a unique and practical solution that can be implemented using MATLAB Processes. Through simulation results, we demonstrate the effectiveness of the TDC technique in suppressing time delay and enhancing control performance, resulting in a significant improvement in extraction efficiency. This work contributes to the field of sugar production by introducing a specialized TDC technique tailored for the juice extraction process in sugar mills.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139103129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based optimal EVCS integration with stochastically sized and distributed PVs in an RDNS segmented in zones","authors":"Ebunle Akupan Rene, Willy Stephen Tounsi Fokui","doi":"10.1186/s43067-023-00126-w","DOIUrl":"https://doi.org/10.1186/s43067-023-00126-w","url":null,"abstract":"The growing interest in electric vehicles (EVs) for transportation has led to increased production and government support through legislation since they offer environmental benefits such as reduced air pollution and carbon emissions compared to conventional combustion engine vehicles. This shift toward EV technology aligns with the goal of preserving the natural environment. To fully utilize EVs, effective management of the power grid is crucial, particularly in radial distribution network systems (RDNS) as they pose stress and deviation of power system parameters from their normal. This study proposes a novel strategy for maximizing EV utilization through EV charging stations (EVCSs) in an RDNS by considering factors such as load voltage deviation, line losses, and the presence of distributed solar photovoltaic systems at load centers. The research begins by segmenting the RDNS into zones, followed by the application of an artificial intelligence-based hybrid genetic algorithm (GA) and particle swarm optimization (PSO) approach known as hybrid GA–PSO. This approach identifies optimal locations for EVCSs integrated with photovoltaics within the network. Subsequently, the employment of individual GA and PSO algorithms to optimize EVCS placement focuses on minimizing power loss and enhancing voltage. The effectiveness of the hybrid GA–PSO algorithm is compared to that of separate GA and PSO methods. Extensive simulations using the IEEE 33-node test feeders validate the proposed techniques, demonstrating the usefulness of the hybrid GA–PSO algorithm in identifying optimal EVCS placement within each zone. The results also highlight the advantages and novelty of hybrid GA–PSO in achieving optimal EVCS placement with stochastically sized and distributed photovoltaic in an RDNS.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139077978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A market definition paradigm equilibrium voltage analysis of ageing and temperature in lithium-ion cells","authors":"Samuel O. Enochoghene, Thomas K. Yesufu","doi":"10.1186/s43067-023-00127-9","DOIUrl":"https://doi.org/10.1186/s43067-023-00127-9","url":null,"abstract":"This study was on the use of the market definition paradigm (MDP) to track ageing and temperature effects in lithium–ion cells. This was with a view to using the technique to obtain a sequence of equilibrium voltages from readily available datasets in order to profile the effects of ageing and temperature on cells and batteries. The method employed involved using the MDP with its capability to obtain a sequence of equilibrium voltages for lithium-ion cells. This approach integrated radio incidence with radio geometry, transmission and emergence in a simplified form of the cell’s equilibrium voltage (and amperage). A standard dataset was obtained from the centre for advanced life cycle engineering repository. The data were processed and analysed using Coulomb counting, charging and discharging energy comparison methods on Python 3.8 programming tool and LibreOffice spreadsheet software. Results obtained show a close tracking of ageing and temperature phenomena in the cells studied. A respective maximum and minimum equilibrium voltages of 3.23 V and 3.10 V over two thousand (2000) cycles were similarly obtained for ageing and temperature investigations. The equilibrium voltage shows a downward trend as the battery ages and is more reliable for studies on these cells than the open circuit voltage traditionally used to track phenomena in such cells. In conclusion, typical lithium-ion cells can be classified at begin-of-life using the equilibrium voltage and useful predictions made with respect to end-of-life. This approach is relatively inexpensive, requiring fewer data points and low-cost hardware and extensible to online applications. ","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139078172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on action recognition for accident detection in smart city transportation systems","authors":"Victor A. Adewopo, Nelly Elsayed, Zag ElSayed, Murat Ozer, Ahmed Abdelgawad, Magdy Bayoumi","doi":"10.1186/s43067-023-00124-y","DOIUrl":"https://doi.org/10.1186/s43067-023-00124-y","url":null,"abstract":"Accident detection and public traffic safety is a crucial aspect of safe and better community. Monitoring traffic flow in smart cities using different surveillance cameras plays a crucial role in recognizing accidents and alerting first responders. In computer vision tasks, utilizing action recognition (AR) has contributed to high-precision video surveillance, medical imaging, and digital signal processing applications. This paper presents an intensive review focusing on action recognition in accident detection and autonomous transportation systems for smart city. This paper focused on AR systems that use diverse sources of traffic video, such as static surveillance cameras on traffic intersections, highway monitoring cameras, drone cameras, and dash-cams. Through this review, we identified the primary techniques, taxonomies, and algorithms used in AR for autonomous transportation and accident detection. We also examined datasets utilized in the AR tasks, identifying the primary sources of datasets and features of the datasets. This paper provides a potential research direction to develop and integrate accident detection systems for autonomous cars and public traffic safety systems by alerting emergency personnel and law enforcement in the event of road traffic accidents to minimize the human error in accident reporting and provide a spontaneous response to victims.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"83 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment analysis from textual data using multiple channels deep learning models","authors":"Adepu Rajesh, Tryambak Hiwarkar","doi":"10.1186/s43067-023-00125-x","DOIUrl":"https://doi.org/10.1186/s43067-023-00125-x","url":null,"abstract":"Text sentiment analysis has been of great importance over the last few years. It is being widely used to determine a person’s feelings, opinions and emotions on any topic or for someone. In recent years, convolutional neural networks (CNNs) and long short-term memory (LSTM) have been widely adopted to develop such models. CNN has shown that it can effectively extract local information between consecutive words, but it lacks in extracting contextual semantic information between words. However, LSTM is able to extract some contextual information, where it lacks in extracting local information. To counter such problems, we applied the attention mechanism in our multi-channel CNN with bidirectional LSTM model to give attention to those parts of sentence which have major influence in determining the sentiment of that sentence. Experimental results show that our multi-channel CNN model with bidirectional LSTM and attention mechanism achieved an accuracy of 94.13% which outperforms the traditional CNN, LSTM + CNN and other machine learning algorithms.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}