In this paper an observer-based controller is developed for continuous-time spacecraft attitude control system. Firstly, a high gain observer is created for the system model. Then, the estimated states are used to generate a backstepping controller to regulate the performance of sate variables to the desired states. Then, the nonlinear closed loop control system is simulated to demonstrate the effectiveness of the developed control approach in regulating the performance of the system to the desired steady state.
{"title":"An Observer-Based Backstepping Controller for Spacecraft Attitude Control System","authors":"E. Aljuwaiser","doi":"10.46338/ijetae0623_01","DOIUrl":"https://doi.org/10.46338/ijetae0623_01","url":null,"abstract":"In this paper an observer-based controller is developed for continuous-time spacecraft attitude control system. Firstly, a high gain observer is created for the system model. Then, the estimated states are used to generate a backstepping controller to regulate the performance of sate variables to the desired states. Then, the nonlinear closed loop control system is simulated to demonstrate the effectiveness of the developed control approach in regulating the performance of the system to the desired steady state.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"586 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131277763","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}
Kielvien Lourensius Eka Setia Putra, Fabian Surya Pramudya, A. A. Gunawan, Prasetyo Mimboro
—Nitrogen is a crucial nutrient for the sustainable health and productivity of oil palm plantations. Accurate fertilization for Nitrogencan optimize production while reducing maintenance costs. This study investigates the relationship between various vegetation indices and oil palm Nitrogen content using aerial images. We employ and compare different machine learning algorithms to predict Nitrogen content in oil palms, utilizing RGB aerial images obtained from PT. Perkebunan Nusantara IV (PTPN IV) in North Sumatra. Twelve vegetation indices are assessed, considering the limited spectral information available from the aerial images. Our findings reveal that the random forest algorithm, when applied to Hue, Green Leaf Index, and Coloration Index, yields the highest prediction accuracy of 90.13%. Furthermore, the results demonstrate that machine learning algorithms can effectively overcome the limitations of near-infrared channel availability, allowing for the prediction of Nitrogen content using RGB aerial images as a proxy for chlorophyll absorption.
氮是油棕种植园持续健康和生产力的关键养分。精确的氮肥施肥可以优化产量，同时降低维护成本。利用航拍影像研究了油棕不同植被指数与氮素含量的关系。我们利用北苏门答腊岛PT. Perkebunan Nusantara IV (PTPN IV)获得的RGB航空图像，采用并比较了不同的机器学习算法来预测油棕的氮含量。考虑到航空影像提供的有限光谱信息，对12个植被指数进行了评估。我们的研究结果表明，随机森林算法在色相、绿叶指数和颜色指数上的预测准确率最高，达到90.13%。此外，结果表明，机器学习算法可以有效地克服近红外通道可用性的限制，允许使用RGB航空图像作为叶绿素吸收的代理来预测氮含量。
{"title":"Predicting Nitrogen Content in Oil Palms through Machine Learning and RGB Aerial Imagery","authors":"Kielvien Lourensius Eka Setia Putra, Fabian Surya Pramudya, A. A. Gunawan, Prasetyo Mimboro","doi":"10.46338/ijetae0623_03","DOIUrl":"https://doi.org/10.46338/ijetae0623_03","url":null,"abstract":"—Nitrogen is a crucial nutrient for the sustainable health and productivity of oil palm plantations. Accurate fertilization for Nitrogencan optimize production while reducing maintenance costs. This study investigates the relationship between various vegetation indices and oil palm Nitrogen content using aerial images. We employ and compare different machine learning algorithms to predict Nitrogen content in oil palms, utilizing RGB aerial images obtained from PT. Perkebunan Nusantara IV (PTPN IV) in North Sumatra. Twelve vegetation indices are assessed, considering the limited spectral information available from the aerial images. Our findings reveal that the random forest algorithm, when applied to Hue, Green Leaf Index, and Coloration Index, yields the highest prediction accuracy of 90.13%. Furthermore, the results demonstrate that machine learning algorithms can effectively overcome the limitations of near-infrared channel availability, allowing for the prediction of Nitrogen content using RGB aerial images as a proxy for chlorophyll absorption.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125401653","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}
Adaptive learning allows students to learn effectively based on their abilities and characteristics at their own pace. Currently, numerous SLRs exist regarding students' adaptive learning characteristics, analyzed from various perspectives by previous researchers. However, it is essential to note that the findings of these studies are based on analyses conducted between 2010 and 2020. This SLR aims to extend and bridge the gaps in identifying essential student characteristics for implementing an adaptive learning system. It specifically focuses on the most recent five-year period, from 2018 to 2022. This study chose 39 articles according to the specified inclusion and exclusion criteria. The findings from the SLR indicate that learning style is the most commonly used element of adaptation in the reviewed articles, followed by knowledge characteristics, cognitive traits, student preference, and motivation. This SLR also revealed that most of the reviewed articles used more than one student's characteristics in modeling the student model, and results show that educators integrated online learning for implementing adaptive learning in the teaching and learning process. The ILS instrument, which is traditional detection, is widely used in collecting learning style data, and besides FSLSM, other learning style models, including VARK, Kolb, and Honey & Mumford, are used to assign students' learning style preferences.
{"title":"Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review","authors":"Rahimah A. Halim, R. Mohemad, N. Ali","doi":"10.46338/ijetae0623_02","DOIUrl":"https://doi.org/10.46338/ijetae0623_02","url":null,"abstract":"Adaptive learning allows students to learn effectively based on their abilities and characteristics at their own pace. Currently, numerous SLRs exist regarding students' adaptive learning characteristics, analyzed from various perspectives by previous researchers. However, it is essential to note that the findings of these studies are based on analyses conducted between 2010 and 2020. This SLR aims to extend and bridge the gaps in identifying essential student characteristics for implementing an adaptive learning system. It specifically focuses on the most recent five-year period, from 2018 to 2022. This study chose 39 articles according to the specified inclusion and exclusion criteria. The findings from the SLR indicate that learning style is the most commonly used element of adaptation in the reviewed articles, followed by knowledge characteristics, cognitive traits, student preference, and motivation. This SLR also revealed that most of the reviewed articles used more than one student's characteristics in modeling the student model, and results show that educators integrated online learning for implementing adaptive learning in the teaching and learning process. The ILS instrument, which is traditional detection, is widely used in collecting learning style data, and besides FSLSM, other learning style models, including VARK, Kolb, and Honey & Mumford, are used to assign students' learning style preferences.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133248224","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}
—Efficiency in photovoltaic (PV) energy production is significantly influenced by various electrical, environmental, and manufacturing-related factors. These variables often lead to a range of PV generator faults, compromising the system's performance and the overall grid's safety. The current fault detection methods can be complex and resource-intensive. In this paper, we propose a novel and efficient grid-connected PV system fault detection mechanism using the k-means clustering algorithm. Our approach categorizes the possible faults based on clustering the output PV and grid powers under healthy and faulty conditions. A comparison between centroid locations of both conditions leads to fault categorization. The findings demonstrate the efficacy of the proposed technique for addressing localized faults in grid-tied PV systems without the need for complicated calculations. The technique is both cost-effective and accurate, with a straightforward application that can be easily adopted by all stakeholders. This method enables users to safeguard their PV system's health and ensure the more comprehensive grid's safety.
{"title":"Grid Connected PV Systems Fault Detection using K-Means Clustering Algorithm","authors":"Khalil Benmouiza","doi":"10.46338/ijetae0523_07","DOIUrl":"https://doi.org/10.46338/ijetae0523_07","url":null,"abstract":"—Efficiency in photovoltaic (PV) energy production is significantly influenced by various electrical, environmental, and manufacturing-related factors. These variables often lead to a range of PV generator faults, compromising the system's performance and the overall grid's safety. The current fault detection methods can be complex and resource-intensive. In this paper, we propose a novel and efficient grid-connected PV system fault detection mechanism using the k-means clustering algorithm. Our approach categorizes the possible faults based on clustering the output PV and grid powers under healthy and faulty conditions. A comparison between centroid locations of both conditions leads to fault categorization. The findings demonstrate the efficacy of the proposed technique for addressing localized faults in grid-tied PV systems without the need for complicated calculations. The technique is both cost-effective and accurate, with a straightforward application that can be easily adopted by all stakeholders. This method enables users to safeguard their PV system's health and ensure the more comprehensive grid's safety.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114854435","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}
S. Arifin, AndyanKalmer Wijaya, R. Nariswari, I. Yudistira, S. Suwarno, Faisal Faisal, Diah Wihardini
-One of the most widely used machine learning methods, Long Short-Term Memory (LSTM), is particularly useful for time series prediction. In this study, we carried out a bibliometric analysis against publications about LSTMs to identify trends and contributions of researchers in the development of machine learning technology. We collect bibliometric data from the Scopus database and use the bibliometric analysis method to analyze trends and contributions of researchers in publications about LSTM. Results of the bibliometric analysis show that LSTM is a lot used in related machine learning applications with time series data and is one the most popular technique for use in predictions. In addition, the use of LSTM is often combined with other deep learning methods, such as neural networks, to improve accuracy prediction. In addition, the results of the bibliometric analysis also show that the use of LSTM has spread to various fields, such as in handwriting recognition, processing Language experience, and recognition of a face. Implications from the results of this study are that the use of LSTM can provide solutions that are accurate and effective in solving prediction problems in various fields, especially in practical applications such as business, health, and transportation. The results of the LSTM bibliometric analysis can provide a broader view of trends and the contributions of researchers to the development of machine learning technology, as well as identify potential research areas for further development. Therefore, this research provides an important contribution to strengthening the results of previous research and showing that the use of LSTM has great potential in the development of future machine learning technology
{"title":"Long Short-Term Memory (LSTM): Trends and Future Research Potential","authors":"S. Arifin, AndyanKalmer Wijaya, R. Nariswari, I. Yudistira, S. Suwarno, Faisal Faisal, Diah Wihardini","doi":"10.46338/ijetae0523_04","DOIUrl":"https://doi.org/10.46338/ijetae0523_04","url":null,"abstract":"-One of the most widely used machine learning methods, Long Short-Term Memory (LSTM), is particularly useful for time series prediction. In this study, we carried out a bibliometric analysis against publications about LSTMs to identify trends and contributions of researchers in the development of machine learning technology. We collect bibliometric data from the Scopus database and use the bibliometric analysis method to analyze trends and contributions of researchers in publications about LSTM. Results of the bibliometric analysis show that LSTM is a lot used in related machine learning applications with time series data and is one the most popular technique for use in predictions. In addition, the use of LSTM is often combined with other deep learning methods, such as neural networks, to improve accuracy prediction. In addition, the results of the bibliometric analysis also show that the use of LSTM has spread to various fields, such as in handwriting recognition, processing Language experience, and recognition of a face. Implications from the results of this study are that the use of LSTM can provide solutions that are accurate and effective in solving prediction problems in various fields, especially in practical applications such as business, health, and transportation. The results of the LSTM bibliometric analysis can provide a broader view of trends and the contributions of researchers to the development of machine learning technology, as well as identify potential research areas for further development. Therefore, this research provides an important contribution to strengthening the results of previous research and showing that the use of LSTM has great potential in the development of future machine learning technology","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"31 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123258240","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}
—Automated Teller Machine (ATM) frauds have increased over the years resulting in the loss of customers and trust in current ATM security measures. In order to fight identity fraud and regain trust and reliability, biometric recognition technologies have become a necessity for ATMs. Using a card and pin is no longer reliable due to the rise of ATM identity fraud over the years. Studies have been carried out to solve this uprising issue, such as replacing the memorized pin with a one-time pin (OTP) and others using biometrics. There are still many gaps and challenges in the systems, with a common challenge being their accuracy rate, dependencies, and reliability. This study is conducted to alleviate the problem by introducing a versatile multimodal biometrics system. After a careful review of different biometric techniques and their differences in human identification inboth accuracy and reliability, physical biometrics isadopted in this study. Unlike behaviouralbiometrics, physical biometric features cannot easily be copied, replicated, or mimicked, and they are unique per individual. The three body features chosen for this study are the face, fingerprint, and iris. A real-time feature quality evaluation method is employed to assess the reliability of the biometric recognition results. The system provides independent anytime ATM access and prevents card-based theft and fraud, it can be relied on to always deliver uninterrupted ATM services to customers. Customers register their fingerprint, face, and iris to be able to use the biometric ATMs. A combination of any two of the registered biometric features can be used to authenticate users with high accuracy and reliability on ATMs with a 0% false acceptance rate. It has been found that allowing multiple options for users reduces false rejections and provides a 100% ATM access guarantee.
{"title":"Versatile Multimodal Biometrics to Prevent ATM Identity Frauds","authors":"Tumelo Presley Nkgapele, Chunling Tu, Moses Olaifa","doi":"10.46338/ijetae0523_01","DOIUrl":"https://doi.org/10.46338/ijetae0523_01","url":null,"abstract":"—Automated Teller Machine (ATM) frauds have increased over the years resulting in the loss of customers and trust in current ATM security measures. In order to fight identity fraud and regain trust and reliability, biometric recognition technologies have become a necessity for ATMs. Using a card and pin is no longer reliable due to the rise of ATM identity fraud over the years. Studies have been carried out to solve this uprising issue, such as replacing the memorized pin with a one-time pin (OTP) and others using biometrics. There are still many gaps and challenges in the systems, with a common challenge being their accuracy rate, dependencies, and reliability. This study is conducted to alleviate the problem by introducing a versatile multimodal biometrics system. After a careful review of different biometric techniques and their differences in human identification inboth accuracy and reliability, physical biometrics isadopted in this study. Unlike behaviouralbiometrics, physical biometric features cannot easily be copied, replicated, or mimicked, and they are unique per individual. The three body features chosen for this study are the face, fingerprint, and iris. A real-time feature quality evaluation method is employed to assess the reliability of the biometric recognition results. The system provides independent anytime ATM access and prevents card-based theft and fraud, it can be relied on to always deliver uninterrupted ATM services to customers. Customers register their fingerprint, face, and iris to be able to use the biometric ATMs. A combination of any two of the registered biometric features can be used to authenticate users with high accuracy and reliability on ATMs with a 0% false acceptance rate. It has been found that allowing multiple options for users reduces false rejections and provides a 100% ATM access guarantee.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121518146","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}
R. A. A. L Rajamohan, Sivakumar Subramaniam, Farah Shahnaz Feroz, R. Sujatha
The pipeline is one of the main modes oftransportation for the oil and gas industry that could be laid offshore and onshore. Pipeline transportation requires close monitoring as a failure in pipelines causes disruption of supply, loss of precious commodities and not to mention irreversible environmental damage will be an essential topic. The major issue in AODV and DSDV is when the network gets more extensive, which causes high packet traffic resulting in high packet loss, energy consumption and passive nodes. This research aims to establish a routing algorithm with better performance focusing on throughput, fairness index, delivery ratio and passive nodes and energy consumption. The study is confined to wireless sensor network simulation on Network Simulator Tool. A technique of quad-interleaving Ad-hoc On- demand Distance Vector (QAODV) routing algorithm has been developed where predefined Alpha, Beta, Charlie and Delta nodes will communicate with the nodes which belong to their family. The results of the developed routing protocol have been compared with standard AODV, DSDV and OLSR routing protocols. The developed routing algorithm has produced a massive improvement in the delivery ratio (25% more), throughput (44% more), passive nodes (71 less), fairness index (0.06 more) and energy consumption (0.22J less).
{"title":"Quad-Interleaving Routing Algorithm for Performance Enhancement of Oil and Gas Pipeline Network Monitoring","authors":"R. A. A. L Rajamohan, Sivakumar Subramaniam, Farah Shahnaz Feroz, R. Sujatha","doi":"10.46338/ijetae0523_03","DOIUrl":"https://doi.org/10.46338/ijetae0523_03","url":null,"abstract":"The pipeline is one of the main modes oftransportation for the oil and gas industry that could be laid offshore and onshore. Pipeline transportation requires close monitoring as a failure in pipelines causes disruption of supply, loss of precious commodities and not to mention irreversible environmental damage will be an essential topic. The major issue in AODV and DSDV is when the network gets more extensive, which causes high packet traffic resulting in high packet loss, energy consumption and passive nodes. This research aims to establish a routing algorithm with better performance focusing on throughput, fairness index, delivery ratio and passive nodes and energy consumption. The study is confined to wireless sensor network simulation on Network Simulator Tool. A technique of quad-interleaving Ad-hoc On- demand Distance Vector (QAODV) routing algorithm has been developed where predefined Alpha, Beta, Charlie and Delta nodes will communicate with the nodes which belong to their family. The results of the developed routing protocol have been compared with standard AODV, DSDV and OLSR routing protocols. The developed routing algorithm has produced a massive improvement in the delivery ratio (25% more), throughput (44% more), passive nodes (71 less), fairness index (0.06 more) and energy consumption (0.22J less).","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127970549","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}
Currently, data and its exploitation play a very important role in the life of individuals and organizations, without forgetting that through data management tools these organizations store their data history, which guides them at the operational and strategic levels. Considering the explosion of data at huge sizes, the old relational system has several shortcomings in managing this information, which justifies the need for data migration to NoSQL systems, which manage BigData efficiently. In this sense, this article fits to find a new transformation of relational databases to MongoDB, as a wellchosen NoSQL system for this transformation. This article presents the problem of this transformation, so that it can be discussed based on related works, then enlightened by presenting the storage philosophy and the semantics of the two systems source and destination of this transformation, which will be chained by our approach which defining a set of transformation rules to keep the same data and advantages of relational systems under a structure adequate to the concept of MongoDB and finally our conclusion of this work.
{"title":"New Structural and Semantic Transformation of Relational Databases to MongoDB","authors":"Abdelhak Erraji, A. Maizate, M. Ouzzif","doi":"10.46338/ijetae0523_06","DOIUrl":"https://doi.org/10.46338/ijetae0523_06","url":null,"abstract":"Currently, data and its exploitation play a very important role in the life of individuals and organizations, without forgetting that through data management tools these organizations store their data history, which guides them at the operational and strategic levels. Considering the explosion of data at huge sizes, the old relational system has several shortcomings in managing this information, which justifies the need for data migration to NoSQL systems, which manage BigData efficiently. In this sense, this article fits to find a new transformation of relational databases to MongoDB, as a wellchosen NoSQL system for this transformation. This article presents the problem of this transformation, so that it can be discussed based on related works, then enlightened by presenting the storage philosophy and the semantics of the two systems source and destination of this transformation, which will be chained by our approach which defining a set of transformation rules to keep the same data and advantages of relational systems under a structure adequate to the concept of MongoDB and finally our conclusion of this work.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127631898","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}
Chester L. Cofino, M. M. P. Cruz, Ken M. Balogo, Jefrey G. Alegia, Van Roger B. Gutib, Ryan B. Escorial, F. Wenceslao, Jr.
ICT security has proven to be important in any organization in dealing with digital data. In implementing data protection, there are several challenges that an organization may encounter such as employee’s lack of awareness and education, cybersecurity threats, data breaches, lack of technical infrastructure, and limited resources. To combat internal security threats and encourage employees' security habits every agency, in the different sectors of the government must practice and promote data protection awareness against cybercrimes. To improve the security posture of every public or private organization in the Philippines. This study looked at a newly suggested security management standard that offers a thorough framework for detecting and evaluating risks to ICT (information and communication technology) systems and applications. The proposed standard strongly emphasizes the necessity of ongoing security control monitoring and assessment, frequent recovery plan testing and evaluation, and compliance with the PDCA Model anchored to the ISO/IEC 27001 standard and the Data Privacy Act of 2012. The study examined the suggested standard's main aspects and potential business advantages, including security, compliance, and stakeholder coordination and communication as well as emphasized the difficulties in implementing the suggested standard, including the requirement for significant resources and knowledge. The proposed standard also provides a common language for communication and collaboration among stakeholders, including I.T. staff, business leaders, and external partners. This can help promote a security culture and ensure everyone in the organization works together towards a common goal.
{"title":"New Management Standard for Digital Data Protection Using A PDCA Model Anchored to ISO/IEC 27001 and R.A. 10173","authors":"Chester L. Cofino, M. M. P. Cruz, Ken M. Balogo, Jefrey G. Alegia, Van Roger B. Gutib, Ryan B. Escorial, F. Wenceslao, Jr.","doi":"10.46338/ijetae0523_08","DOIUrl":"https://doi.org/10.46338/ijetae0523_08","url":null,"abstract":"ICT security has proven to be important in any organization in dealing with digital data. In implementing data protection, there are several challenges that an organization may encounter such as employee’s lack of awareness and education, cybersecurity threats, data breaches, lack of technical infrastructure, and limited resources. To combat internal security threats and encourage employees' security habits every agency, in the different sectors of the government must practice and promote data protection awareness against cybercrimes. To improve the security posture of every public or private organization in the Philippines. This study looked at a newly suggested security management standard that offers a thorough framework for detecting and evaluating risks to ICT (information and communication technology) systems and applications. The proposed standard strongly emphasizes the necessity of ongoing security control monitoring and assessment, frequent recovery plan testing and evaluation, and compliance with the PDCA Model anchored to the ISO/IEC 27001 standard and the Data Privacy Act of 2012. The study examined the suggested standard's main aspects and potential business advantages, including security, compliance, and stakeholder coordination and communication as well as emphasized the difficulties in implementing the suggested standard, including the requirement for significant resources and knowledge. The proposed standard also provides a common language for communication and collaboration among stakeholders, including I.T. staff, business leaders, and external partners. This can help promote a security culture and ensure everyone in the organization works together towards a common goal.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"102 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121054263","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}
Meadi Mohamed Nadjib, Ouamane ferial, Zerari Abd El Moumene, Djeffal Abdelhamid
—Electricity theft is one of the biggest problems facing energy companies. These companies eventually claimed that the conventional methods for preventing electricity fraud were insufficient, which led to the creation of systems based on artificial intelligence to identify thieves among electricity consumers. In this paper, we propose a system based on deep learning to identify customers who have engaged in fraudulent activity on smart grids. We selected one-dimensional (1D) and twodimensional (2D) convolutional neural network models from deep learning models to achieve our objective. Also, we proposed a new method to fill in missing values in the data set. Our findings show that our models enhance the performance of systems that identify electricity thieves.
{"title":"Deep Learning Models for Efficient Detection of Electricity Fraud in Smart Grids","authors":"Meadi Mohamed Nadjib, Ouamane ferial, Zerari Abd El Moumene, Djeffal Abdelhamid","doi":"10.46338/ijetae0523_09","DOIUrl":"https://doi.org/10.46338/ijetae0523_09","url":null,"abstract":"—Electricity theft is one of the biggest problems facing energy companies. These companies eventually claimed that the conventional methods for preventing electricity fraud were insufficient, which led to the creation of systems based on artificial intelligence to identify thieves among electricity consumers. In this paper, we propose a system based on deep learning to identify customers who have engaged in fraudulent activity on smart grids. We selected one-dimensional (1D) and twodimensional (2D) convolutional neural network models from deep learning models to achieve our objective. Also, we proposed a new method to fill in missing values in the data set. Our findings show that our models enhance the performance of systems that identify electricity thieves.","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126523904","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}
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