Int. J. Model. Simul. Sci. Comput.最新文献_第7页

Research on defect detection of improved target detection algorithm on the image surface of 5G communication ring 改进目标检测算法在5G通信环图像表面缺陷检测研究

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-07 DOI: 10.1142/s1793962323410118

Hui Zhu

引用次数: 2

Multiclass cyber-attack classification approach based on the Krill Herd Optimized Deep Neural Network (KH-DNN) model for WSN 基于Krill Herd优化深度神经网络(KH-DNN)模型的WSN多类网络攻击分类方法

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-07 DOI: 10.1142/s1793962322500568

Samleti Sandeep Dwarkanath, R. Aruna

引用次数: 0

Performance analysis with self-organizing map and recurrent neural network 基于自组织映射和递归神经网络的性能分析

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-07 DOI: 10.1142/s1793962322500593

Yongquan Yan

引用次数: 0

Fuzzy control decision-making framework adapted to the uncertainty environment of complex software system 适应复杂软件系统不确定性环境的模糊控制决策框架

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-03-07 DOI: 10.1142/s1793962322410100

Jingjing He, Liwei Zheng, Zhanqi Cui

引用次数: 0

Sorting operation method of manipulator based on deep reinforcement learning 基于深度强化学习的机械手分拣操作方法

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-02-25 DOI: 10.1142/s1793962323410076

Qing An, Yanhua Chen, Hui Zeng, J. Wang

{"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":null,"pages":null},"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}

引用次数: 1

A novel hybrid machine learning-based frequent item extraction for transactional database 一种新的基于混合机器学习的事务性数据库频繁项提取方法

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-02-14 DOI: 10.1142/s1793962323410064

D. Rao, V. Sucharita

引用次数: 0

Identification of industrial air compression system using neural network 工业空气压缩系统的神经网络辨识

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-01-31 DOI: 10.1142/s179396232250043x

Fan-Hao Khong, Md Fahmi Abd Samad, Brahmataran Tamadaran

{"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":null,"pages":null},"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}

引用次数: 0

A hybrid elephant herding optimization and harmony search algorithm for potential load balancing in cloud environments 一种用于云环境中潜在负载平衡的混合象群优化和和谐搜索算法

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-01-31 DOI: 10.1142/s1793962322500428

Syed Muqthadar Ali, N. Kumaran, G. N. Balaji

{"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":null,"pages":null},"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}

引用次数: 1

Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model 区块链具有支持深度学习的安全医疗数据传输和诊断模型

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-01-20 DOI: 10.1142/s1793962322410069

S. Neelakandan, J. Beulah, L. Prathiba, G. Murthy, E. F. I. Raj, N. Arulkumar

{"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":null,"pages":null},"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}

引用次数: 23

Optimal prediction of user mobility based on spatio-temporal matching 基于时空匹配的用户移动性优化预测

Int. J. Model. Simul. Sci. Comput. Pub Date : 2022-01-18 DOI: 10.1142/s1793962322500465

A. Ajinu, C. P. Maheswaran

引用次数: 0