Pub Date : 2023-02-09DOI: 10.15837/ijccc.2023.1.4396
R. Murugesan, Karthikeyan Madhu, Jayalakshmi Sambandam, L. Malliga
In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being directed on the analysis of COVID-19 data within the framework of the machines learning method. Researchers looked the COVID 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. This might prompt governments worldwide to develop strategies for protecting the health of the public. Previous systems rely on Long Short- Term Memory (LSTM) networks for predicting new instances of COVID-19. The LSTM network findings suggest that the pandemic might be over by June of 2020. However, LSTM may have an over-fitting issue, and it may fall short of expectations in terms of true positive. For this issue in COVID-19 forecasting, we suggest using two methods such as Cat Swarm Optimization (CSO) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. In this proposed study, we take the COVID-19 predicting database as an contribution and normalise it using the min-max approach. The accuracy of classification is improved with the use of the first method to choose the optimal features. In this method, inertia weight is added to the CSO optimization algorithm convergence. Death and confirmed cases are predicted for a certain time period throughout India using Convolutional Neural Network with Partial Binomial Distribution based on carefully chosen characteristics. The experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy.
{"title":"Covid-19 Forecasting Using CNN Approach With A Halbinomial Distribution And A Linear Decreasing Inertia Weight-Based Cat Swarm Optimization","authors":"R. Murugesan, Karthikeyan Madhu, Jayalakshmi Sambandam, L. Malliga","doi":"10.15837/ijccc.2023.1.4396","DOIUrl":"https://doi.org/10.15837/ijccc.2023.1.4396","url":null,"abstract":"In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being directed on the analysis of COVID-19 data within the framework of the machines learning method. Researchers looked the COVID 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. This might prompt governments worldwide to develop strategies for protecting the health of the public. Previous systems rely on Long Short- Term Memory (LSTM) networks for predicting new instances of COVID-19. The LSTM network findings suggest that the pandemic might be over by June of 2020. However, LSTM may have an over-fitting issue, and it may fall short of expectations in terms of true positive. For this issue in COVID-19 forecasting, we suggest using two methods such as Cat Swarm Optimization (CSO) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. In this proposed study, we take the COVID-19 predicting database as an contribution and normalise it using the min-max approach. The accuracy of classification is improved with the use of the first method to choose the optimal features. In this method, inertia weight is added to the CSO optimization algorithm convergence. Death and confirmed cases are predicted for a certain time period throughout India using Convolutional Neural Network with Partial Binomial Distribution based on carefully chosen characteristics. The experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131032674","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 : 2023-02-09DOI: 10.15837/ijccc.2023.1.5062
W. Pan, Cheng Liu
Multi-Agent Reinforcement Learning (MARL) is widely used to solve various real-world problems. In MARL, the environment contains multiple agents. A good grasp of the environment can guide agents to learn cooperative strategies. In Centralized Training Decentralized Execution (CTDE), a centralized critic is used to guide cooperative strategies learning. However, having multiple agents in the environment leads to the curse of dimensionality and influence of other agents’ strategies, resulting in difficulties for centralized critics to learn good cooperative strategies. We propose a graph-based approach to overcome the above problems. It uses a graph neural network, which uses partial observations of agents as input, and information between agents is aggregated by graph methods to extract information about the whole environment. In this way, agents can improve their understanding of the overall state of the environment and other agents in the environment while avoiding dimensional explosion. Then we combine a dual critic dynamic decomposition method with soft actor-critic to train policy. The former uses individual and global rewards for learning, avoiding the influence of other agents’ strategies, and the latter help to learn an optional policy better. We call this approach Multi-Agent Graph-based soft Actor-Critic (MAGAC). We compare our proposed method with several classical MARL algorithms under the Multi-agent Particle Environment (MPE). The experimental results show that our method can achieve a faster learning speed while learning better policy.
{"title":"A Graph-Based Soft Actor Critic Approach in Multi-Agent Reinforcement Learning","authors":"W. Pan, Cheng Liu","doi":"10.15837/ijccc.2023.1.5062","DOIUrl":"https://doi.org/10.15837/ijccc.2023.1.5062","url":null,"abstract":"Multi-Agent Reinforcement Learning (MARL) is widely used to solve various real-world problems. In MARL, the environment contains multiple agents. A good grasp of the environment can guide agents to learn cooperative strategies. In Centralized Training Decentralized Execution (CTDE), a centralized critic is used to guide cooperative strategies learning. However, having multiple agents in the environment leads to the curse of dimensionality and influence of other agents’ strategies, resulting in difficulties for centralized critics to learn good cooperative strategies. We propose a graph-based approach to overcome the above problems. It uses a graph neural network, which uses partial observations of agents as input, and information between agents is aggregated by graph methods to extract information about the whole environment. In this way, agents can improve their understanding of the overall state of the environment and other agents in the environment while avoiding dimensional explosion. Then we combine a dual critic dynamic decomposition method with soft actor-critic to train policy. The former uses individual and global rewards for learning, avoiding the influence of other agents’ strategies, and the latter help to learn an optional policy better. We call this approach Multi-Agent Graph-based soft Actor-Critic (MAGAC). We compare our proposed method with several classical MARL algorithms under the Multi-agent Particle Environment (MPE). The experimental results show that our method can achieve a faster learning speed while learning better policy.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128904055","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 : 2023-02-09DOI: 10.15837/ijccc.2023.1.4977
Ahmed Banimustafa, Rozan AbdelHalim, Olla Bulkrock, Ahmad Al-Hmouz
Most concrete structures suffer from degradation, where cracks are the most obvious visual sign. Concrete structures must be continuously monitored and assessed to avoid further deterioration, which may lead to a partial or total collapse. This is particularly important when constructing large structures such as towers, bridges, tunnels, and dams. This work aims to demonstrate and evaluate several deep learning approaches that can be used for monitoring and assessing the level of concrete degradation based on the cracks’ visual signs, which can then be embedded in Health Monitoring Systems (SHM). The experimental work in this study involves creating three models: Two were built using ResNet-50 and Xception transfer learning networks. In contrast, the third was built using a customized Sequential Convolutional Neural Network (SCNN) architecture. The dataset comprises 2,000 image samples sampled from a larger dataset that contains 56,000 images and which belong to four severity classes: minor, moderate, and severe, in addition to a normal class for no crack signs. The SCNN model achieved an accuracy of 90.2%, while the Xception and ResNet-50 models scored an accuracy of 86.3% and 70%, respectively.
{"title":"Deep Learning for Assessing Severity of Cracks in Concrete Structures","authors":"Ahmed Banimustafa, Rozan AbdelHalim, Olla Bulkrock, Ahmad Al-Hmouz","doi":"10.15837/ijccc.2023.1.4977","DOIUrl":"https://doi.org/10.15837/ijccc.2023.1.4977","url":null,"abstract":"Most concrete structures suffer from degradation, where cracks are the most obvious visual sign. Concrete structures must be continuously monitored and assessed to avoid further deterioration, which may lead to a partial or total collapse. This is particularly important when constructing large structures such as towers, bridges, tunnels, and dams. This work aims to demonstrate and evaluate several deep learning approaches that can be used for monitoring and assessing the level of concrete degradation based on the cracks’ visual signs, which can then be embedded in Health Monitoring Systems (SHM). The experimental work in this study involves creating three models: Two were built using ResNet-50 and Xception transfer learning networks. In contrast, the third was built using a customized Sequential Convolutional Neural Network (SCNN) architecture. The dataset comprises 2,000 image samples sampled from a larger dataset that contains 56,000 images and which belong to four severity classes: minor, moderate, and severe, in addition to a normal class for no crack signs. The SCNN model achieved an accuracy of 90.2%, while the Xception and ResNet-50 models scored an accuracy of 86.3% and 70%, respectively.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131184960","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 : 2023-02-09DOI: 10.15837/ijccc.2023.1.4478
S. Muthurajkumar, S. Nisha
Spam is characterized as unnecessary and garbage E-mails. Due to the increasing of unsolicited E-mails, it is becoming more and more crucial for mail users to utilize a trustworthy spam E-mail filter. The shortcomings of spam classifier are defined by their increasing inability to manage large amounts of relevant messages and to effectively detect and effectively detect spam messages. Numerous characteristics in spam classifications are problematic. Given that selecting features is one of the most often used and successful techniques for feature reduction, it is a crucial duty in the identification of keyword content. As a result, features that are unnecessary and pointless yet potentially harm effciency would be removed. In this study, we present SGNNCNN (Semantic Graph Neural Network With CNN) as a solution to tackle the diffcult task of mail identification. By projections E-mails onto a graph and by using the SGNN-CNN model for classifications, this technique transforms the E-mail classification issue into a graph classification challenge. There is no need to integrate the word into a representation since the E-mail characteristics are produced from the semantic network. On several open databases, the technique's effectiveness is evaluated. Some few public databases were used in experiments to demonstrate the high accuracy of the proposed approach for classifying E-mails. In term of spam classification, the performance is superior to state-of-the-art deep learning-based methods.
垃圾邮件的特点是不必要的和垃圾电子邮件。由于不请自来的电子邮件越来越多，对于邮件用户来说，使用可靠的垃圾邮件过滤器变得越来越重要。垃圾邮件分类器的缺点是越来越不能管理大量的相关消息，不能有效地检测和有效地检测垃圾邮件。垃圾邮件分类中的许多特征都存在问题。考虑到选择特征是最常用和最成功的特征缩减技术之一，它是识别关键字内容的关键任务。因此，那些不必要的、毫无意义的、但可能损害效率的功能将被删除。在这项研究中，我们提出了SGNNCNN (Semantic Graph Neural Network With CNN)作为解决邮件识别困难任务的解决方案。通过将电子邮件投影到图上并使用SGNN-CNN模型进行分类，该技术将电子邮件分类问题转化为图分类挑战。由于E-mail特征是由语义网络产生的，因此不需要将单词集成到表示中。在几个开放数据库上，对该技术的有效性进行了评价。在实验中使用了一些公共数据库，以证明所提出的方法对电子邮件进行分类的准确性。在垃圾邮件分类方面，性能优于最先进的基于深度学习的方法。
{"title":"Semantic Graph Based Convolutional Neural Network for Spam e-mail Classification in Cybercrime Applications","authors":"S. Muthurajkumar, S. Nisha","doi":"10.15837/ijccc.2023.1.4478","DOIUrl":"https://doi.org/10.15837/ijccc.2023.1.4478","url":null,"abstract":"Spam is characterized as unnecessary and garbage E-mails. Due to the increasing of unsolicited E-mails, it is becoming more and more crucial for mail users to utilize a trustworthy spam E-mail filter. The shortcomings of spam classifier are defined by their increasing inability to manage large amounts of relevant messages and to effectively detect and effectively detect spam messages. Numerous characteristics in spam classifications are problematic. Given that selecting features is one of the most often used and successful techniques for feature reduction, it is a crucial duty in the identification of keyword content. As a result, features that are unnecessary and pointless yet potentially harm effciency would be removed. In this study, we present SGNNCNN (Semantic Graph Neural Network With CNN) as a solution to tackle the diffcult task of mail identification. By projections E-mails onto a graph and by using the SGNN-CNN model for classifications, this technique transforms the E-mail classification issue into a graph classification challenge. There is no need to integrate the word into a representation since the E-mail characteristics are produced from the semantic network. On several open databases, the technique's effectiveness is evaluated. Some few public databases were used in experiments to demonstrate the high accuracy of the proposed approach for classifying E-mails. In term of spam classification, the performance is superior to state-of-the-art deep learning-based methods.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133572938","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 : 2023-02-09DOI: 10.15837/ijccc.2023.1.5111
C. Nicolae, Rohan Kumar Yadav, D. Tufis
Natural language processing (NLP) has become a vital requirement in a wide range of applications, including machine translation, information retrieval, and text classification. The development and evaluation of NLP models for various languages have received significant attention in recent years, but there has been relatively little work done on comparing the performance of different language models on Romanian data. In particular, the introduction and evaluation of various Romanian language models with multilingual models have barely been comparatively studied. In this paper, we address this gap by evaluating eight NLP models on two Romanian datasets, XQuAD and RoITD. Our experiments and results show that bert-base-multilingual-cased and bertbase- multilingual-uncased, perform best on both XQuAD and RoITD tasks, while RoBERT-small model and DistilBERT models perform the worst. We also discuss the implications of our findings and outline directions for future work in this area.
{"title":"Evaluation of Language Models on Romanian XQuAD and RoITD datasets","authors":"C. Nicolae, Rohan Kumar Yadav, D. Tufis","doi":"10.15837/ijccc.2023.1.5111","DOIUrl":"https://doi.org/10.15837/ijccc.2023.1.5111","url":null,"abstract":"Natural language processing (NLP) has become a vital requirement in a wide range of applications, including machine translation, information retrieval, and text classification. The development and evaluation of NLP models for various languages have received significant attention in recent years, but there has been relatively little work done on comparing the performance of different language models on Romanian data. In particular, the introduction and evaluation of various Romanian language models with multilingual models have barely been comparatively studied. In this paper, we address this gap by evaluating eight NLP models on two Romanian datasets, XQuAD and RoITD. Our experiments and results show that bert-base-multilingual-cased and bertbase- multilingual-uncased, perform best on both XQuAD and RoITD tasks, while RoBERT-small model and DistilBERT models perform the worst. We also discuss the implications of our findings and outline directions for future work in this area.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124798330","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 : 2023-02-09DOI: 10.15837/ijccc.2023.1.4981
F. Alfayez, H. Bouhamed
For dependent population, falls accident are a serious health issue, particularly in a situation of pandemic saturation of health structures. It is, therefore, highly desirable to quarantine patients at home, in order to avoid the spread of contagious diseases. A dedicated surveillance system at home may become an urgent need in order to improve the patients’ living autonomy and significantly reduce assistance costs while preserving their privacy and intimacy. The domestic fall accident is regarded as an abrupt pose transition. Accordingly, normal human postures have to be recognized first. To this end, we proposed a novel big data scalable method for posture recognition using uniform local binary pattern (uLBP) histograms for pattern extraction. Instead of saving the pixels of the entire image, only the patterns were kept for the identification of human postures. By doing so, we tried to preserve people’s intimacy, which is very important in ehealth. To our knowledge, our work is the first to use this approach in a big data platform context for fall event detection while using Random Forest instead of complex deep learning methods. Application results of our conduct are very interesting in comparison to complex architectures such as convolutional deep neural networks (CNN) and feedforward deep neural networks (DFFNN).
{"title":"Machine learning and uLBP histograms for posture recognition of dependent people via Big Data Hadoop and Spark platform","authors":"F. Alfayez, H. Bouhamed","doi":"10.15837/ijccc.2023.1.4981","DOIUrl":"https://doi.org/10.15837/ijccc.2023.1.4981","url":null,"abstract":"For dependent population, falls accident are a serious health issue, particularly in a situation of pandemic saturation of health structures. It is, therefore, highly desirable to quarantine patients at home, in order to avoid the spread of contagious diseases. A dedicated surveillance system at home may become an urgent need in order to improve the patients’ living autonomy and significantly reduce assistance costs while preserving their privacy and intimacy. The domestic fall accident is regarded as an abrupt pose transition. Accordingly, normal human postures have to be recognized first. To this end, we proposed a novel big data scalable method for posture recognition using uniform local binary pattern (uLBP) histograms for pattern extraction. Instead of saving the pixels of the entire image, only the patterns were kept for the identification of human postures. By doing so, we tried to preserve people’s intimacy, which is very important in ehealth. To our knowledge, our work is the first to use this approach in a big data platform context for fall event detection while using Random Forest instead of complex deep learning methods. Application results of our conduct are very interesting in comparison to complex architectures such as convolutional deep neural networks (CNN) and feedforward deep neural networks (DFFNN).","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"111 3S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126889380","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 : 2022-12-14DOI: 10.15837/ijccc.2022.6.4910
Shenqi Gao, Shuxin Wang, Weigang Pan, Mushu Wang, Song Gao
In order to improve the detection accuracy of the navigation line by the unmanned automatic marking vehicle (UAMV) in the complex construction environment. Solve the problem of unqualified road markings drawn by the UAMV due to inaccurate detection during construction. A navigation line detection algorithm based on and improved YOLOv4 and improved Haar-like feature named YOLOv4-HR is proposed in this paper. Firstly, an image enhancement algorithm based on improved Haar-like features is proposed. It is used to enhance the images of the training set, make the images contain more semantic information, which improves the generalization ability of the network; Secondly, a multi-scale feature extraction network is added to the YOLOv4 network, which made model has a stronger learning ability for details and improves the accuracy of detection. Finally, a verification experiment is carried out on the self-built data set. The experimental results show that, compared with the original YOLOv4 network, the method proposed in this paper improves the AP value by 14.3% and the recall by 11.89%. The influence of factors such as the environment on the detection effect of the navigation line is reduced, and the effect of the navigation line detection in the visual navigation of the UAMV is effectively improved.
{"title":"Based on Haar-like feature and improved YOLOv4 navigation line detection algorithm in complex environment","authors":"Shenqi Gao, Shuxin Wang, Weigang Pan, Mushu Wang, Song Gao","doi":"10.15837/ijccc.2022.6.4910","DOIUrl":"https://doi.org/10.15837/ijccc.2022.6.4910","url":null,"abstract":"In order to improve the detection accuracy of the navigation line by the unmanned automatic marking vehicle (UAMV) in the complex construction environment. Solve the problem of unqualified road markings drawn by the UAMV due to inaccurate detection during construction. A navigation line detection algorithm based on and improved YOLOv4 and improved Haar-like feature named YOLOv4-HR is proposed in this paper. Firstly, an image enhancement algorithm based on improved Haar-like features is proposed. It is used to enhance the images of the training set, make the images contain more semantic information, which improves the generalization ability of the network; Secondly, a multi-scale feature extraction network is added to the YOLOv4 network, which made model has a stronger learning ability for details and improves the accuracy of detection. Finally, a verification experiment is carried out on the self-built data set. The experimental results show that, compared with the original YOLOv4 network, the method proposed in this paper improves the AP value by 14.3% and the recall by 11.89%. The influence of factors such as the environment on the detection effect of the navigation line is reduced, and the effect of the navigation line detection in the visual navigation of the UAMV is effectively improved.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127287975","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 : 2022-12-14DOI: 10.15837/ijccc.2022.6.4588
Song Liu, Runlan Zhang, Yongheng Shi
Micro-structured sensors that can perceive and communicate at the same time have emerged as a result of the quick growth of microelectronics technology, wireless communication technology, and sensor technology. This little gadget has the ability to sense many types of environmental data, gather it at the sink node, and then send it to the data centre. In the civic, industrial, agricultural, military, and other domains, wireless sensor networks are frequently employed. A virtual force model of curved surface ecological monitoring network for moving coverage is presented, and a moving coverage algorithm for curved surface ecological monitoring network is given, according to the actual needs of curved surface ecological monitoring, such as grasslands, wetlands, deserts, and coastal beaches. The moving coverage algorithm of curved surface ecology monitoring network pushes the sensor nodes to the uncovered area on the monitored surface and fixes the monitoring blind zone on the monitored surface using a virtual force between sensor nodes in the ecological monitoring network. The moving coverage process of the moving coverage algorithm of the ecological monitoring network is simulated in order to verify the efficiency of the moving coverage algorithm of curved surface ecological monitoring network. The simulation results demonstrate that the moving coverage algorithm suggested in this paper can successfully increase the coverage of the ecological monitoring network on the monitoring surface by precisely locating the monitoring blind zone of the ecological monitoring network and pushing the sensor nodes to the monitoring blind zone for coverage. The final coverage ratio is greater than 95%, and the node deployment phase’s coverage ratio can reach 85% to 90%.
{"title":"Design of Moving Coverage Algorithm of Ecological Monitoring Network for Curved Surface","authors":"Song Liu, Runlan Zhang, Yongheng Shi","doi":"10.15837/ijccc.2022.6.4588","DOIUrl":"https://doi.org/10.15837/ijccc.2022.6.4588","url":null,"abstract":"Micro-structured sensors that can perceive and communicate at the same time have emerged as a result of the quick growth of microelectronics technology, wireless communication technology, and sensor technology. This little gadget has the ability to sense many types of environmental data, gather it at the sink node, and then send it to the data centre. In the civic, industrial, agricultural, military, and other domains, wireless sensor networks are frequently employed. A virtual force model of curved surface ecological monitoring network for moving coverage is presented, and a moving coverage algorithm for curved surface ecological monitoring network is given, according to the actual needs of curved surface ecological monitoring, such as grasslands, wetlands, deserts, and coastal beaches. The moving coverage algorithm of curved surface ecology monitoring network pushes the sensor nodes to the uncovered area on the monitored surface and fixes the monitoring blind zone on the monitored surface using a virtual force between sensor nodes in the ecological monitoring network. The moving coverage process of the moving coverage algorithm of the ecological monitoring network is simulated in order to verify the efficiency of the moving coverage algorithm of curved surface ecological monitoring network. The simulation results demonstrate that the moving coverage algorithm suggested in this paper can successfully increase the coverage of the ecological monitoring network on the monitoring surface by precisely locating the monitoring blind zone of the ecological monitoring network and pushing the sensor nodes to the monitoring blind zone for coverage. The final coverage ratio is greater than 95%, and the node deployment phase’s coverage ratio can reach 85% to 90%.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116584822","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 : 2022-12-14DOI: 10.15837/ijccc.2022.6.4988
Mingyang Zhang, Jiayue Lu, Ning Ma, T. Cheng, Guowei Hua
The global e-commerce market is growing at a rapid pace, but the percentage of repeat buyers is low. According to Tmall, the repurchase rate is only 6.1%, while research shows that a 5% increase in the repurchase rate can lead to a 25% to 95% increase in profit. To increase the repurchase rate, merchants need to predict potential repeat buyers and convert them into repurchasers. Therefore, it is necessary to predict repeat buyers. In this paper we build a prediction model of repeat purchasers using Tmall’s dataset. First, we build high-quality feature engineering for e-commerce scenarios by manual construction and algorithmic selection. We introduce the synthetic minority oversampling technique (SMOTE) algorithm to solve the data imbalance problem and improve prediction performance. Then we train classical classifiers including factorization machine and logistic regression, and ensemble learning classifiers including extreme gradient boosting, and light gradient boosting machine machines. Finally, we construct a two-layer fusion model based on the Stacking algorithm to further enhance prediction performance. The results show that through a series of innovations such as data imbalance processing, feature engineering, and fusion models, the model area under curve (AUC) value is improved by 0.01161. Our findings provide important implications for managing e-commerce platforms and the platform merchants.
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Pub Date : 2022-12-14DOI: 10.15837/ijccc.2022.6.4929
D. Saida, P. Premchand
The identification of brain tumor consumes time and therefore it is important to develop an automated system using an imaging technique. The classification of brain tumor into benign or malignant is performed by using Magnetic Resonance Image (MRI). From the MRI based brain tumor images, the extraction of features is essential for pattern recognition that determines the object based on the color, names, shapes, or more. Therefore, the classifiers are dependent on the strength of features such as shape, color, etc., Yet, the classifiers are dependent on the features that are extracted using deep learning classifiers which are dependent on the features that were extracted. The deep learning algorithm in the medical domain showed interest in the computer vision researchers which consumed time during the process of execution. The proposed Dilated UNet model expands the receptive field for the extraction of multi scale context information. Based on the high resolution conditions, the large scale feature maps and high-resolution conditions are generated using large scale feature maps. It provides rich spatial information that was applied for performing semantic segmentation. Semantic image segmentation is achieved using a U-Net as it adds an expansive path to generate classifications of the pixels belonging to features found in the source image. The existing Kernel based SVM model obtained accuracy of 99.15%, Non-Dominated Sorted Genetic Algorithm-Convolutional Neural Network (NSGA -CNN) obtained accuracy of 99%, Deep Elman Neural network with adaptive fuzzy clustering obtained accuracy of 98%, 3D Context Deep Supervised U-Net obtained accuracy of 92%. Whereas, the proposed Dilated U-Net-based CNN model obtained accuracy of 99.5% better when compared with the existing models.
脑肿瘤的识别需要时间，因此开发一种使用成像技术的自动化系统是很重要的。脑肿瘤的良性或恶性分类是通过磁共振成像(MRI)进行的。从基于MRI的脑肿瘤图像中，特征的提取对于基于颜色、名称、形状或更多来确定目标的模式识别至关重要。因此，分类器依赖于特征的强度，如形状、颜色等。然而，分类器依赖于使用深度学习分类器提取的特征，而深度学习分类器依赖于提取的特征。医学领域的深度学习算法引起了计算机视觉研究者的兴趣，但在执行过程中耗费了大量的时间。提出的扩展UNet模型扩展了多尺度上下文信息提取的接受域。基于高分辨率条件，利用大比例尺特征图生成大比例尺特征图和高分辨率条件。它为语义分割提供了丰富的空间信息。语义图像分割是使用U-Net实现的，因为它添加了一个扩展路径来生成属于源图像中发现的特征的像素分类。现有基于核的SVM模型准确率为99.15%，非支配排序遗传算法-卷积神经网络(NSGA -CNN)准确率为99%，自适应模糊聚类的Deep Elman神经网络准确率为98%，3D Context Deep Supervised U-Net准确率为92%。与现有模型相比，本文提出的基于扩展u - net的CNN模型的准确率提高了99.5%。
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