Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.01408107
Khiem H. G, K. V, Huong H. L, Quy T. L, P. N., N. K, T. N., B. K, Trong D. P. N., Hieu M. D., Bao Q. T., Khoa D. T.
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{"title":"Implementing a Blockchain, Smart Contract, and NFT Framework for Waste Management Systems in Emerging Economies: An Investigation in Vietnam","authors":"Khiem H. G, K. V, Huong H. L, Quy T. L, P. N., N. K, T. N., B. K, Trong D. P. N., Hieu M. D., Bao Q. T., Khoa D. T.","doi":"10.14569/ijacsa.2023.01408107","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01408107","url":null,"abstract":".","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"31 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77811186","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140654
Nazirah Abd. Hamid, S. R. Selamat, R. Ahmad, M. Mohamad
Role-based Access Control has become the standard of practice for many organizations for restricting control on limited resources in complicated infrastructures or systems. The main objective of the role mining development is to define appropriate roles that can be applied to the specified security access policies. However, the mining scales in this kind of setting are extensive and can cause a huge load on the management of the systems. To resolve the above mentioned problems, this paper proposes a model that implements Hamming Distance approach by rearranging the existing matrix as the input data to overcome the scalability problem. The findings of the model show that the generated file size of all datasets substantially have been reduced compared to the original datasets It has also shown that Hamming Distance technique can successfully reduce the mining scale of datasets ranging between 30% and 47% and produce better candidate roles. Keywords—Role-based Access Control; role mining; hamming distance; data mining
{"title":"Hamming Distance Approach to Reduce Role Mining Scalability","authors":"Nazirah Abd. Hamid, S. R. Selamat, R. Ahmad, M. Mohamad","doi":"10.14569/ijacsa.2023.0140654","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140654","url":null,"abstract":"Role-based Access Control has become the standard of practice for many organizations for restricting control on limited resources in complicated infrastructures or systems. The main objective of the role mining development is to define appropriate roles that can be applied to the specified security access policies. However, the mining scales in this kind of setting are extensive and can cause a huge load on the management of the systems. To resolve the above mentioned problems, this paper proposes a model that implements Hamming Distance approach by rearranging the existing matrix as the input data to overcome the scalability problem. The findings of the model show that the generated file size of all datasets substantially have been reduced compared to the original datasets It has also shown that Hamming Distance technique can successfully reduce the mining scale of datasets ranging between 30% and 47% and produce better candidate roles. Keywords—Role-based Access Control; role mining; hamming distance; data mining","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"116 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80438654","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.01406125
Endang Wahyu Pamungkas, Divi Galih Prasetyo Putri, A. Fatmawati
This study aims to provide an overview of the current research on detecting abusive language in Indonesian social media. The study examines existing datasets, methods, and challenges and opportunities in this field. The research found that most existing datasets for detecting abusive language were collected from social media platforms such as Twitter, Facebook, and Instagram, with Twitter being the most commonly used source. The study also found that hate speech is the most researched type of abusive language. Various models, including traditional machine learning and deep learning approaches, have been implemented for this task, with deep learning models showing more competitive results. However, the use of transformer-based models is less popular in Indonesian hate speech studies. The study also emphasizes the importance of exploring more diverse phenomena, such as islamophobia and political hate speech. Additionally, the study suggests crowdsourcing as a potential solution for the annotation approach for labeling datasets. Furthermore, it encourages researchers to consider code-mixing issues in abusive language datasets in Indonesia, as it could improve the overall model performance for detecting abusive language in Indonesian data. The study also suggests that the lack of effective regulations and the anonymity afforded to users on most social networking sites, as well as the increasing number of Twitter users in Indonesia, have contributed to the rising prevalence of hate speech in Indonesian social media. The study also notes the importance of considering code-mixed language, out-of-vocabulary words, grammatical errors, and limited context when working with social media data. Keywords—Abusive language; hate speech detection; machine learning; social media
{"title":"Hate Speech Detection in Bahasa Indonesia: Challenges and Opportunities","authors":"Endang Wahyu Pamungkas, Divi Galih Prasetyo Putri, A. Fatmawati","doi":"10.14569/ijacsa.2023.01406125","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01406125","url":null,"abstract":"This study aims to provide an overview of the current research on detecting abusive language in Indonesian social media. The study examines existing datasets, methods, and challenges and opportunities in this field. The research found that most existing datasets for detecting abusive language were collected from social media platforms such as Twitter, Facebook, and Instagram, with Twitter being the most commonly used source. The study also found that hate speech is the most researched type of abusive language. Various models, including traditional machine learning and deep learning approaches, have been implemented for this task, with deep learning models showing more competitive results. However, the use of transformer-based models is less popular in Indonesian hate speech studies. The study also emphasizes the importance of exploring more diverse phenomena, such as islamophobia and political hate speech. Additionally, the study suggests crowdsourcing as a potential solution for the annotation approach for labeling datasets. Furthermore, it encourages researchers to consider code-mixing issues in abusive language datasets in Indonesia, as it could improve the overall model performance for detecting abusive language in Indonesian data. The study also suggests that the lack of effective regulations and the anonymity afforded to users on most social networking sites, as well as the increasing number of Twitter users in Indonesia, have contributed to the rising prevalence of hate speech in Indonesian social media. The study also notes the importance of considering code-mixed language, out-of-vocabulary words, grammatical errors, and limited context when working with social media data. Keywords—Abusive language; hate speech detection; machine learning; social media","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"5 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76312984","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140802
R. Dornala
— Edge computing has gained significant attention in recent years due to its ability to process data closer to the source, resulting in reduced latency and improved performance. However, ensuring data security and efficient data management in edge-based computing applications poses significant challenges. This paper proposes an ensemble security approach and a multi-cloud load-balancing strategy to address these challenges. The ensemble security approach leverages multiple security mechanisms, such as encryption, authentication, and intrusion detection systems, to provide a layered defense against potential threats. By combining these mechanisms, the system can detect and mitigate security breaches at various levels, ensuring the integrity and confidentiality of data in edge-based environments. The multi-cloud load balancing strategy also aims to optimize resource utilization and performance by distributing data processing tasks across multiple cloud service providers. This approach takes advantage of the flexibility and scalability offered by the cloud, allowing for dynamic workload allocation based on factors like network conditions and computational capabilities. To evaluate the effectiveness of the proposed approach, we conducted experiments using a realistic edge-based computing environment. The results demonstrate that the ensemble security approach effectively detects and prevents security threats, while the multi-cloud load balancing strategy with edge computing to improve the overall system performance and resource utilization.
{"title":"Ensemble Security and Multi-Cloud Load Balancing for Data in Edge-based Computing Applications","authors":"R. Dornala","doi":"10.14569/ijacsa.2023.0140802","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140802","url":null,"abstract":"— Edge computing has gained significant attention in recent years due to its ability to process data closer to the source, resulting in reduced latency and improved performance. However, ensuring data security and efficient data management in edge-based computing applications poses significant challenges. This paper proposes an ensemble security approach and a multi-cloud load-balancing strategy to address these challenges. The ensemble security approach leverages multiple security mechanisms, such as encryption, authentication, and intrusion detection systems, to provide a layered defense against potential threats. By combining these mechanisms, the system can detect and mitigate security breaches at various levels, ensuring the integrity and confidentiality of data in edge-based environments. The multi-cloud load balancing strategy also aims to optimize resource utilization and performance by distributing data processing tasks across multiple cloud service providers. This approach takes advantage of the flexibility and scalability offered by the cloud, allowing for dynamic workload allocation based on factors like network conditions and computational capabilities. To evaluate the effectiveness of the proposed approach, we conducted experiments using a realistic edge-based computing environment. The results demonstrate that the ensemble security approach effectively detects and prevents security threats, while the multi-cloud load balancing strategy with edge computing to improve the overall system performance and resource utilization.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76622033","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140816
B.O. Malasowe, M. Akazue, Ejaita Abugor Okpako, Fidelis Obukowho Aghware, A. Ojugo, Dr. Ojie
—The post covid-19 studies have reported significant negative impact witnessed on global education and learning with the closure of schools’ physical infrastructure from 2020 to 2022. Its effects today continues to ripple across the learning processes even with advances in e-learning or media literacy. The adoption and integration therein of e-learning on the Nigerian frontier is yet to be fully harnessed. From traditional to blended learning, and to virtual learning – Nigeria must rise, and develop new strategies to address issues with her educational theories as well as to bridge the gap and negative impact of the post covid-19 pandemic. This study implements a virtual learning framework that adequately fuses the alternative delivery asynchronous-learning with traditional synchronous learning for adoption in the Nigerian Educational System. Result showcases improved cognition in learners, engaged qualitative learning
{"title":"Adaptive Learner-CBT with Secured Fault-Tolerant and Resumption Capability for Nigerian Universities","authors":"B.O. Malasowe, M. Akazue, Ejaita Abugor Okpako, Fidelis Obukowho Aghware, A. Ojugo, Dr. Ojie","doi":"10.14569/ijacsa.2023.0140816","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140816","url":null,"abstract":"—The post covid-19 studies have reported significant negative impact witnessed on global education and learning with the closure of schools’ physical infrastructure from 2020 to 2022. Its effects today continues to ripple across the learning processes even with advances in e-learning or media literacy. The adoption and integration therein of e-learning on the Nigerian frontier is yet to be fully harnessed. From traditional to blended learning, and to virtual learning – Nigeria must rise, and develop new strategies to address issues with her educational theories as well as to bridge the gap and negative impact of the post covid-19 pandemic. This study implements a virtual learning framework that adequately fuses the alternative delivery asynchronous-learning with traditional synchronous learning for adoption in the Nigerian Educational System. Result showcases improved cognition in learners, engaged qualitative learning","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"252 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78527235","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140426
Xiuli Yan
Recommendation algorithms can greatly improve the efficiency of information retrieval for users. This article briefly introduced recommendation algorithms based on association rules and algorithms based on interest and emotion analysis. After crawling music and comment data from the NetEase Cloud platform, a simulation experiment was conducted. Firstly, the performance of the Back-Propagation Neural Network (BPNN) in the interest and emotion-based algorithm for recommending music was tested, and then the impact of the proportion of emotion weight between comments and music on the emotion analysis-based algorithm was tested. Finally, the three recommendation algorithms based on association rules, user ratings, and interest and emotion analysis were compared. The results showed that when the BPNN used the dominant interest and emotion and secondary interest and emotion as judgment criteria, the accuracy of interest and emotion recognition for music and comments was higher. When the proportion of interest and emotion weight between comments and music was 6:4, the interest and emotion analysis-based recommendation algorithm had the highest accuracy. The interest and emotion-based recommendation algorithm had higher recommendation accuracy than the association rule-based and user rating-based algorithms, and could provide users with more personalized and emotional music recommendations. Keywords—Interest and emotion; recommendation algorithm; music; personalization
{"title":"Personalized Music Recommendation Based on Interest and Emotion: A Comparison of Multiple Algorithms","authors":"Xiuli Yan","doi":"10.14569/ijacsa.2023.0140426","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140426","url":null,"abstract":"Recommendation algorithms can greatly improve the efficiency of information retrieval for users. This article briefly introduced recommendation algorithms based on association rules and algorithms based on interest and emotion analysis. After crawling music and comment data from the NetEase Cloud platform, a simulation experiment was conducted. Firstly, the performance of the Back-Propagation Neural Network (BPNN) in the interest and emotion-based algorithm for recommending music was tested, and then the impact of the proportion of emotion weight between comments and music on the emotion analysis-based algorithm was tested. Finally, the three recommendation algorithms based on association rules, user ratings, and interest and emotion analysis were compared. The results showed that when the BPNN used the dominant interest and emotion and secondary interest and emotion as judgment criteria, the accuracy of interest and emotion recognition for music and comments was higher. When the proportion of interest and emotion weight between comments and music was 6:4, the interest and emotion analysis-based recommendation algorithm had the highest accuracy. The interest and emotion-based recommendation algorithm had higher recommendation accuracy than the association rule-based and user rating-based algorithms, and could provide users with more personalized and emotional music recommendations. Keywords—Interest and emotion; recommendation algorithm; music; personalization","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"9 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78602295","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":"Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem","authors":"Hachemi Bennaceur, Meznah Almutairy, Nora Alqhtani","doi":"10.14569/ijacsa.2023.0140333","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140333","url":null,"abstract":"org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"7 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78663318","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140110
Abdalilah. G. I. Alhalangy
—In recent years, the city of Kassala has suffered from frequent flooding disasters in the Gash River, which is the city's lifeblood. But the problem of frequent flooding of the river has made it a life-threatening nightmare. The importance of research lies in the fact that it is one of the few attempts to discuss and study the causes and effects of the Gash River floods. It aims to identify the factors affecting river floods. It proposes an algorithm to simulate flooding by randomly generating different factors that effectively affect river flooding. The descriptive analytical approach, the analytical, inductive approach, and the analytical deductive approach to desk research were used, taking advantage of the primary statistical method in its observation and evaluation, which relies on primary and secondary information to help make scientific, practical, and objective. The research came out with significant results related to the problems that threaten the town of Kassala from the frequent floods of the Gash River. The study's results proved that there is a deviation and discrepancy between the floods rate during the year, which gives a negative indication, and that deposited quantities vary in different proportions from one period to another, which causes a significant threat in the future. The research suggests other solutions that help reduce the problems and their effects. In addition to the above, the study proposes various recommendations that will be the basis for future studies to reach the required solutions and goals.
{"title":"Developing a Computer Simulation to Study the Behavior of Factors Affecting the Flooding of the Gash River","authors":"Abdalilah. G. I. Alhalangy","doi":"10.14569/ijacsa.2023.0140110","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140110","url":null,"abstract":"—In recent years, the city of Kassala has suffered from frequent flooding disasters in the Gash River, which is the city's lifeblood. But the problem of frequent flooding of the river has made it a life-threatening nightmare. The importance of research lies in the fact that it is one of the few attempts to discuss and study the causes and effects of the Gash River floods. It aims to identify the factors affecting river floods. It proposes an algorithm to simulate flooding by randomly generating different factors that effectively affect river flooding. The descriptive analytical approach, the analytical, inductive approach, and the analytical deductive approach to desk research were used, taking advantage of the primary statistical method in its observation and evaluation, which relies on primary and secondary information to help make scientific, practical, and objective. The research came out with significant results related to the problems that threaten the town of Kassala from the frequent floods of the Gash River. The study's results proved that there is a deviation and discrepancy between the floods rate during the year, which gives a negative indication, and that deposited quantities vary in different proportions from one period to another, which causes a significant threat in the future. The research suggests other solutions that help reduce the problems and their effects. In addition to the above, the study proposes various recommendations that will be the basis for future studies to reach the required solutions and goals.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"168 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76892042","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140844
Jin-Il Han
—Under the background of the continuous progress of Industry 4.0 reform, the market demand for mobile robots in major world economies is gradually increasing. In order to improve the mobile robot's movement path planning quality and obstacle avoidance ability, this research adjusted the node selection method, pheromone update mechanism, transition probability and volatility coefficient calculation method of the ant colony algorithm, and improved the search direction setting and cost estimation calculation method of the A* algorithm. Thus, a robot movement path planning model can be designed with respect to the improved ant colony algorithm and A* algorithm. The simulation experiment results on grid maps show that the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the Tianniu whisker search algorithm, and the particle swarm algorithm designed in this study converged after 8, 37, 23, and 26 iterations, respectively. The minimum path lengths after convergence were 13.24m, 17.82m, 16.24m, and 17.05m, respectively. When the edge length of the grid map is 100m, the minimum planning length and total moving time of the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the longicorn whisker search algorithm, and the particle swarm algorithm designed in this study are 49m, 104m, 75m, 93m and 49s, 142s, 93s, and 127s, respectively. This indicates that the model designed in this study can effectively shorten the mobile path and training time while completing mobile tasks. The results of this study have a certain reference value for optimizing the robot's movement mode and obstacle avoidance ability.
{"title":"Application of Improved Ant Colony Algorithm Integrating Adaptive Parameter Configuration in Robot Mobile Path Design","authors":"Jin-Il Han","doi":"10.14569/ijacsa.2023.0140844","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140844","url":null,"abstract":"—Under the background of the continuous progress of Industry 4.0 reform, the market demand for mobile robots in major world economies is gradually increasing. In order to improve the mobile robot's movement path planning quality and obstacle avoidance ability, this research adjusted the node selection method, pheromone update mechanism, transition probability and volatility coefficient calculation method of the ant colony algorithm, and improved the search direction setting and cost estimation calculation method of the A* algorithm. Thus, a robot movement path planning model can be designed with respect to the improved ant colony algorithm and A* algorithm. The simulation experiment results on grid maps show that the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the Tianniu whisker search algorithm, and the particle swarm algorithm designed in this study converged after 8, 37, 23, and 26 iterations, respectively. The minimum path lengths after convergence were 13.24m, 17.82m, 16.24m, and 17.05m, respectively. When the edge length of the grid map is 100m, the minimum planning length and total moving time of the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the longicorn whisker search algorithm, and the particle swarm algorithm designed in this study are 49m, 104m, 75m, 93m and 49s, 142s, 93s, and 127s, respectively. This indicates that the model designed in this study can effectively shorten the mobile path and training time while completing mobile tasks. The results of this study have a certain reference value for optimizing the robot's movement mode and obstacle avoidance ability.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"110 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77182836","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}
Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140693
Luyao Wei
In the construction process of high-rise buildings, it is necessary to predict the settlement and deformation of the foundation, and the current prediction methods are mainly based on empirical theoretical calculations and methods and more accurate numerical analysis methods. In the face of the interference of complex and ever-changing terrain and parameter values on prediction methods, in order to accurately determine the settlement of building foundations, this study designed a smart city building foundation settlement prediction method based on BP neural network. Firstly, a real-time dynamic monitoring unit for building foundation settlement was constructed using Wireless Sensor Network (WSN) technology. Then, the monitoring data was used to calculate the relevant parameters of building foundation settlement through layer sum method. Finally, input the monitoring data into the BP network results, adjust the weights of the output layer and hidden layer using settlement related parameters, and output the settlement prediction results of the smart city building foundation through training. The study selected average error and prediction time as evaluation criteria to test the feasibility of the method proposed in this article. This method can effectively predict foundation settlement, with an average prediction error always less than 4% and a prediction process time always less than 49ms. Keyword—Smart city; intelligent architecture; foundation settlement; settlement prediction; BP neural network; parameter
{"title":"Research on Settlement Prediction of Building Foundation in Smart City Based on BP Network","authors":"Luyao Wei","doi":"10.14569/ijacsa.2023.0140693","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140693","url":null,"abstract":"In the construction process of high-rise buildings, it is necessary to predict the settlement and deformation of the foundation, and the current prediction methods are mainly based on empirical theoretical calculations and methods and more accurate numerical analysis methods. In the face of the interference of complex and ever-changing terrain and parameter values on prediction methods, in order to accurately determine the settlement of building foundations, this study designed a smart city building foundation settlement prediction method based on BP neural network. Firstly, a real-time dynamic monitoring unit for building foundation settlement was constructed using Wireless Sensor Network (WSN) technology. Then, the monitoring data was used to calculate the relevant parameters of building foundation settlement through layer sum method. Finally, input the monitoring data into the BP network results, adjust the weights of the output layer and hidden layer using settlement related parameters, and output the settlement prediction results of the smart city building foundation through training. The study selected average error and prediction time as evaluation criteria to test the feasibility of the method proposed in this article. This method can effectively predict foundation settlement, with an average prediction error always less than 4% and a prediction process time always less than 49ms. Keyword—Smart city; intelligent architecture; foundation settlement; settlement prediction; BP neural network; parameter","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"28 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79139772","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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