Pub Date : 2022-03-07DOI: 10.1142/s1793962323410118
Hui Zhu
{"title":"Research on defect detection of improved target detection algorithm on the image surface of 5G communication ring","authors":"Hui Zhu","doi":"10.1142/s1793962323410118","DOIUrl":"https://doi.org/10.1142/s1793962323410118","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"43 1","pages":"2341011:1-2341011:15"},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77310069","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 : 2022-03-07DOI: 10.1142/s1793962322500568
Samleti Sandeep Dwarkanath, R. Aruna
{"title":"Multiclass cyber-attack classification approach based on the Krill Herd Optimized Deep Neural Network (KH-DNN) model for WSN","authors":"Samleti Sandeep Dwarkanath, R. Aruna","doi":"10.1142/s1793962322500568","DOIUrl":"https://doi.org/10.1142/s1793962322500568","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"41 1","pages":"2250056:1-2250056:30"},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77791151","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 : 2022-03-07DOI: 10.1142/s1793962322410100
Jingjing He, Liwei Zheng, Zhanqi Cui
{"title":"Fuzzy control decision-making framework adapted to the uncertainty environment of complex software system","authors":"Jingjing He, Liwei Zheng, Zhanqi Cui","doi":"10.1142/s1793962322410100","DOIUrl":"https://doi.org/10.1142/s1793962322410100","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"38 1","pages":"2241010:1-2241010:19"},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73585809","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 : 2022-02-25DOI: 10.1142/s1793962323410076
Qing An, Yanhua Chen, Hui Zeng, J. Wang
Radioactive waste sorting often faces an unstructured and locally radioactive working environment. At present, remote operation sorting has problems such as low sorting efficiency, greater difficulty in operation, longer training periods for personnel, and poor autonomous control capabilities. Based on the premise of improving the adaptability and autonomous operation ability of robots in an unstructured environment, this paper uses the dual deep Q learning algorithm to optimize the classic deep Q learning algorithm to improve training speed and improve sorting efficiency and stability. Secondly, the sorting algorithm model of deep reinforcement learning is used to determine the optimal behavior in this state. Set up multiple sets of simulations and physical experiments to verify the sorting method. The results show that the robotic arm can autonomously complete sorting tasks under complex conditions and can significantly improve work efficiency when pushing and grasping collaborative operations and will preferentially grasp objects with high radioactivity in the radioactive area. The algorithm has migration ability and good generalization.
{"title":"Sorting operation method of manipulator based on deep reinforcement learning","authors":"Qing An, Yanhua Chen, Hui Zeng, J. Wang","doi":"10.1142/s1793962323410076","DOIUrl":"https://doi.org/10.1142/s1793962323410076","url":null,"abstract":"Radioactive waste sorting often faces an unstructured and locally radioactive working environment. At present, remote operation sorting has problems such as low sorting efficiency, greater difficulty in operation, longer training periods for personnel, and poor autonomous control capabilities. Based on the premise of improving the adaptability and autonomous operation ability of robots in an unstructured environment, this paper uses the dual deep Q learning algorithm to optimize the classic deep Q learning algorithm to improve training speed and improve sorting efficiency and stability. Secondly, the sorting algorithm model of deep reinforcement learning is used to determine the optimal behavior in this state. Set up multiple sets of simulations and physical experiments to verify the sorting method. The results show that the robotic arm can autonomously complete sorting tasks under complex conditions and can significantly improve work efficiency when pushing and grasping collaborative operations and will preferentially grasp objects with high radioactivity in the radioactive area. The algorithm has migration ability and good generalization.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"263 1","pages":"2341007:1-2341007:22"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80094048","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 : 2022-02-14DOI: 10.1142/s1793962323410064
D. Rao, V. Sucharita
In big data, the frequent item set mining is an important framework for many applications. Several techniques were used to mine the frequent item sets, but for the collapsed and complex data, it is difficult. Hence, the current research work aimed to model a novel Frequent Pattern Growth-Hybrid Ant Colony and African Buffalo Model (FPG-HACABM) is developed to overcome this issue and to reduce the execution time. Moreover, the Fitness function of HACABM is utilized to calculate the support count of each item and to improve the classification accuracy. Thus the proposed models classify the frequently utilized items accurately and arranged those items in descending order. This helps to run the big data transactional application effectively without any delay. Finally, the key metrics are validated with the existing models and better results are attained by achieving a high accuracy rate of 99.82% and less execution time of 0.0018 ms.
{"title":"A novel hybrid machine learning-based frequent item extraction for transactional database","authors":"D. Rao, V. Sucharita","doi":"10.1142/s1793962323410064","DOIUrl":"https://doi.org/10.1142/s1793962323410064","url":null,"abstract":"In big data, the frequent item set mining is an important framework for many applications. Several techniques were used to mine the frequent item sets, but for the collapsed and complex data, it is difficult. Hence, the current research work aimed to model a novel Frequent Pattern Growth-Hybrid Ant Colony and African Buffalo Model (FPG-HACABM) is developed to overcome this issue and to reduce the execution time. Moreover, the Fitness function of HACABM is utilized to calculate the support count of each item and to improve the classification accuracy. Thus the proposed models classify the frequently utilized items accurately and arranged those items in descending order. This helps to run the big data transactional application effectively without any delay. Finally, the key metrics are validated with the existing models and better results are attained by achieving a high accuracy rate of 99.82% and less execution time of 0.0018 ms.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"58 1","pages":"2341006:1-2341006:21"},"PeriodicalIF":0.0,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80833691","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 : 2022-01-31DOI: 10.1142/s179396232250043x
Fan-Hao Khong, Md Fahmi Abd Samad, Brahmataran Tamadaran
The existence of random variable in any industrial process is basically unavoidable. It occasionally creates nonlinearity behavior of a system and makes predictive control complicated. Such a random behavior must not be ignored as it may indicate any unknown event occurring during the process. System identification is an approach to construct the mathematical model of a dynamical system using the instrumentation signal of input and output of the system. This study performs system identification by using the NARX model as a base model with the nonlinear functions of a neural network for an industrial air compression system. The identification undergoes a series of analysis (number of neuron, delay and data division) to determine the most suitable NARX-NN model architecture configuration before coming up with a final model. Finally, the validation of model’s predictive performance is carried out through several analyses, namely, mean square error and regression value. The predicted data are compared to the industrial data to verify its accuracy which shows that the final model had successfully ruled out the suspicious random event data.
{"title":"Identification of industrial air compression system using neural network","authors":"Fan-Hao Khong, Md Fahmi Abd Samad, Brahmataran Tamadaran","doi":"10.1142/s179396232250043x","DOIUrl":"https://doi.org/10.1142/s179396232250043x","url":null,"abstract":"The existence of random variable in any industrial process is basically unavoidable. It occasionally creates nonlinearity behavior of a system and makes predictive control complicated. Such a random behavior must not be ignored as it may indicate any unknown event occurring during the process. System identification is an approach to construct the mathematical model of a dynamical system using the instrumentation signal of input and output of the system. This study performs system identification by using the NARX model as a base model with the nonlinear functions of a neural network for an industrial air compression system. The identification undergoes a series of analysis (number of neuron, delay and data division) to determine the most suitable NARX-NN model architecture configuration before coming up with a final model. Finally, the validation of model’s predictive performance is carried out through several analyses, namely, mean square error and regression value. The predicted data are compared to the industrial data to verify its accuracy which shows that the final model had successfully ruled out the suspicious random event data.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"36 1","pages":"2250043:1-2250043:12"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85050903","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 : 2022-01-31DOI: 10.1142/s1793962322500428
Syed Muqthadar Ali, N. Kumaran, G. N. Balaji
In cloud computing environment, load balancing delinquent arises when a large count of new IoT user requests are linked with specific fog nodes. So, a well-organized load balancing tactic is needed in cloud computing. Therefore, in this manuscript, a hybrid elephant herding optimization and harmony search algorithm (HSA) for potential load balancing in cloud environments (HEHO-HSA-PLB-CE) is effectively proposed for reducing task waiting time, load balancing rate, scheduling time, delay and energy consumption. The HEHO algorithm and HSA are mainly used for leveraging the allocation of virtual machine (VM) and incorporating an enhanced strategy of physical machine selection. The proposed HEHO-HSA-PLB-CE method aims at preventing the issue of premature convergence or the issue related to the solution falling at the point of local optimum. Finally, the proposed method potentially achieves load balance under the allocation of VM and enhancement of resource utilization in the cloud computing environment. The proposed approach is activated in CloudSim and the efficiency of the proposed method is assessed by evaluation metrics, such as response time, load balance rate, scheduling time, delay, energy consumption. Then, the simulation performance of the proposed method provides lower delay 32.82%, 25.32%, 29.34% and 34.18%, low energy consumption 38.22%, 25.46%, 42.12% and 15.34% compared with the existing methods, like Aquila optimizer for PLB in CE (AO-PLB-CE), arithmetic optimization algorithm for PLB in CE (AOA-PLB-CE), sine cosine algorithm for PLB in CE (SCA-PLB-CE), and enhanced krill herd algorithm for PLB in CE (EKHO-PLB-CE) respectively.
{"title":"A hybrid elephant herding optimization and harmony search algorithm for potential load balancing in cloud environments","authors":"Syed Muqthadar Ali, N. Kumaran, G. N. Balaji","doi":"10.1142/s1793962322500428","DOIUrl":"https://doi.org/10.1142/s1793962322500428","url":null,"abstract":"In cloud computing environment, load balancing delinquent arises when a large count of new IoT user requests are linked with specific fog nodes. So, a well-organized load balancing tactic is needed in cloud computing. Therefore, in this manuscript, a hybrid elephant herding optimization and harmony search algorithm (HSA) for potential load balancing in cloud environments (HEHO-HSA-PLB-CE) is effectively proposed for reducing task waiting time, load balancing rate, scheduling time, delay and energy consumption. The HEHO algorithm and HSA are mainly used for leveraging the allocation of virtual machine (VM) and incorporating an enhanced strategy of physical machine selection. The proposed HEHO-HSA-PLB-CE method aims at preventing the issue of premature convergence or the issue related to the solution falling at the point of local optimum. Finally, the proposed method potentially achieves load balance under the allocation of VM and enhancement of resource utilization in the cloud computing environment. The proposed approach is activated in CloudSim and the efficiency of the proposed method is assessed by evaluation metrics, such as response time, load balance rate, scheduling time, delay, energy consumption. Then, the simulation performance of the proposed method provides lower delay 32.82%, 25.32%, 29.34% and 34.18%, low energy consumption 38.22%, 25.46%, 42.12% and 15.34% compared with the existing methods, like Aquila optimizer for PLB in CE (AO-PLB-CE), arithmetic optimization algorithm for PLB in CE (AOA-PLB-CE), sine cosine algorithm for PLB in CE (SCA-PLB-CE), and enhanced krill herd algorithm for PLB in CE (EKHO-PLB-CE) respectively.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"85 1","pages":"2250042:1-2250042:23"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82718063","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 : 2022-01-20DOI: 10.1142/s1793962322410069
S. Neelakandan, J. Beulah, L. Prathiba, G. Murthy, E. F. I. Raj, N. Arulkumar
At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2
{"title":"Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model","authors":"S. Neelakandan, J. Beulah, L. Prathiba, G. Murthy, E. F. I. Raj, N. Arulkumar","doi":"10.1142/s1793962322410069","DOIUrl":"https://doi.org/10.1142/s1793962322410069","url":null,"abstract":"At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"2 1","pages":"2241006:1-2241006:16"},"PeriodicalIF":0.0,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83207695","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 : 2022-01-18DOI: 10.1142/s1793962322500465
A. Ajinu, C. P. Maheswaran
{"title":"Optimal prediction of user mobility based on spatio-temporal matching","authors":"A. Ajinu, C. P. Maheswaran","doi":"10.1142/s1793962322500465","DOIUrl":"https://doi.org/10.1142/s1793962322500465","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"6 1","pages":"2250046:1-2250046:29"},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78558390","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：