Pub Date : 2023-01-01DOI: 10.1142/S1793962323500319
S. G. Gollagi
{"title":"Hybrid model with optimization tactics for software defect prediction","authors":"S. G. Gollagi","doi":"10.1142/S1793962323500319","DOIUrl":"https://doi.org/10.1142/S1793962323500319","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81049709","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: 10.1142/S1793962323410167
Neelakandan Subramani, K. Keerthika, P. Ilanchezhian, TamilSelvi Madeswaran, V. Hardas, U. Sakthi
{"title":"Quantum invasive weed optimization-based energy aware task scheduling for cyber-physical system environment","authors":"Neelakandan Subramani, K. Keerthika, P. Ilanchezhian, TamilSelvi Madeswaran, V. Hardas, U. Sakthi","doi":"10.1142/S1793962323410167","DOIUrl":"https://doi.org/10.1142/S1793962323410167","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74149213","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 : 2022-12-27DOI: 10.1142/s1793962322500623
Kasula Raghu, P. Reddy
In recent years, nonorthogonal multiple access (NOMA) has grasped the attention of all researchers in both industrial and academic fields because it has been regarded as an effective solution for 5G technologies to maximize spectral efficiency and connectivity of the system. Also, it has sufficient potential to maximize the performance of a network. Besides, the deployment of NOMA in heterogeneous networks (HetNets) satisfies the requirements of user’s explosive data traffic. However, the increasing demand for energy consumption of the wireless network provokes the researchers to establish an energy-efficient resource allocation scheme. The applications of NOMA provide better utilization of spectrum efficiency and minimize the cost of resource allocation. The resource allocation problem in wireless networks still remains a challenging task as the HetNets are suffered from mutual cross-tier interference. Hence, this research proposes a new effective hybrid optimization-based energy-efficient resource allocation scheme for NOMA HetNets by introducing a newly proposed method called Feedback Water Cycle Algorithm (FWCA). The method evaluates the user-pairing and sub-channel issue for reducing computational complexity. In addition to this, the network is analyzed for determining the power consumption and energy model with dynamic coefficients. Moreover, the developed FWCO obtained the maximum achievable rate of 25.992[Formula: see text]Mbps/Hz, maximum energy efficiency of 77.398%, maximum sum rate of 31.748[Formula: see text]Mbps/Hz, and maximum throughput of 8.888[Formula: see text]Mbps.
{"title":"Energy-efficient resource allocation for NOMA heterogeneous networks using feedback water cycle algorithm","authors":"Kasula Raghu, P. Reddy","doi":"10.1142/s1793962322500623","DOIUrl":"https://doi.org/10.1142/s1793962322500623","url":null,"abstract":"In recent years, nonorthogonal multiple access (NOMA) has grasped the attention of all researchers in both industrial and academic fields because it has been regarded as an effective solution for 5G technologies to maximize spectral efficiency and connectivity of the system. Also, it has sufficient potential to maximize the performance of a network. Besides, the deployment of NOMA in heterogeneous networks (HetNets) satisfies the requirements of user’s explosive data traffic. However, the increasing demand for energy consumption of the wireless network provokes the researchers to establish an energy-efficient resource allocation scheme. The applications of NOMA provide better utilization of spectrum efficiency and minimize the cost of resource allocation. The resource allocation problem in wireless networks still remains a challenging task as the HetNets are suffered from mutual cross-tier interference. Hence, this research proposes a new effective hybrid optimization-based energy-efficient resource allocation scheme for NOMA HetNets by introducing a newly proposed method called Feedback Water Cycle Algorithm (FWCA). The method evaluates the user-pairing and sub-channel issue for reducing computational complexity. In addition to this, the network is analyzed for determining the power consumption and energy model with dynamic coefficients. Moreover, the developed FWCO obtained the maximum achievable rate of 25.992[Formula: see text]Mbps/Hz, maximum energy efficiency of 77.398%, maximum sum rate of 31.748[Formula: see text]Mbps/Hz, and maximum throughput of 8.888[Formula: see text]Mbps.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79515859","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 : 2022-12-23DOI: 10.1142/s179396232350037x
S. Raj, S. Sivagnanam, K. Kumar
{"title":"Design of an IoT platform for data analytics based fault detection and classification in solar PV power plants using CFKC and ODENN","authors":"S. Raj, S. Sivagnanam, K. Kumar","doi":"10.1142/s179396232350037x","DOIUrl":"https://doi.org/10.1142/s179396232350037x","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85038004","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: 10.1142/s1793962322500647
Roshan Gangurde
The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents’ dominating social search creatures’ hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percentage error[Formula: see text]0.45579 for the FIFA dataset. The proposed web page prediction models’ L2 norm and infinity norm are 58.017 and 14, respectively, for the MSNBC_SPMF dataset.
{"title":"Web page prediction using adaptive deer hunting with chicken swarm optimization based neural network model","authors":"Roshan Gangurde","doi":"10.1142/s1793962322500647","DOIUrl":"https://doi.org/10.1142/s1793962322500647","url":null,"abstract":"The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents’ dominating social search creatures’ hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percentage error[Formula: see text]0.45579 for the FIFA dataset. The proposed web page prediction models’ L2 norm and infinity norm are 58.017 and 14, respectively, for the MSNBC_SPMF dataset.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82892807","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 : 2022-09-12DOI: 10.1142/s1793962323500344
P. Velmurugan, B. Ashok
{"title":"Improving the quality of service by continuous traffic monitoring using reinforcement learning model in VANET","authors":"P. Velmurugan, B. Ashok","doi":"10.1142/s1793962323500344","DOIUrl":"https://doi.org/10.1142/s1793962323500344","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74335114","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 : 2022-09-12DOI: 10.1142/s1793962323500356
Razieh Delpasand, M. Hosseini
{"title":"Numerical solution of the three-asset Black-Scholes option pricing model using an efficient hybrid method","authors":"Razieh Delpasand, M. Hosseini","doi":"10.1142/s1793962323500356","DOIUrl":"https://doi.org/10.1142/s1793962323500356","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89953261","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 : 2022-08-24DOI: 10.1142/s1793962323500332
Massoud Nakhkoob
{"title":"A probabilistic model for estimating some design characteristics of aerostructures with an illustration of a Monte Carlo simulation, statistical inferences and applications in aerodynamics","authors":"Massoud Nakhkoob","doi":"10.1142/s1793962323500332","DOIUrl":"https://doi.org/10.1142/s1793962323500332","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76609535","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 : 2022-08-24DOI: 10.1142/s1793962322430061
K. Manjunath, M. C. Sekhar
{"title":"Efficient prediction of future stock values with Gann square using machine learning algorithm","authors":"K. Manjunath, M. C. Sekhar","doi":"10.1142/s1793962322430061","DOIUrl":"https://doi.org/10.1142/s1793962322430061","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82436927","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：