Pub Date : 2021-12-31DOI: 10.1142/s1793962322500313
S. Dar, Anwar Hassan, P. B. Ahmad
In this paper, a new model for count data is introduced by compounding the Poisson distribution with size-biased three-parameter Lindley distribution. Statistical properties, such as reliability, hazard rate, reverse hazard rate, Mills ratio, moments, shewness, kurtosis, moment genrating function, probability generating function and order statistics, have been discussed. Moreover, the collective risk model is discussed by considering the proposed distrubution as the primary distribution and the expoential and Erlang distributions as the secondary ones. Parameter estimation is done using maximum likelihood estimation (MLE). Finally a real dataset is discussed to demonstrate the suitability and applicability of the proposed distribution in modeling count dataset.
{"title":"Poisson size-biased Lindley distribution and its applications","authors":"S. Dar, Anwar Hassan, P. B. Ahmad","doi":"10.1142/s1793962322500313","DOIUrl":"https://doi.org/10.1142/s1793962322500313","url":null,"abstract":"In this paper, a new model for count data is introduced by compounding the Poisson distribution with size-biased three-parameter Lindley distribution. Statistical properties, such as reliability, hazard rate, reverse hazard rate, Mills ratio, moments, shewness, kurtosis, moment genrating function, probability generating function and order statistics, have been discussed. Moreover, the collective risk model is discussed by considering the proposed distrubution as the primary distribution and the expoential and Erlang distributions as the secondary ones. Parameter estimation is done using maximum likelihood estimation (MLE). Finally a real dataset is discussed to demonstrate the suitability and applicability of the proposed distribution in modeling count dataset.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"13 1","pages":"2250031:1-2250031:19"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88059393","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 : 2021-12-29DOI: 10.1142/s1793962322500350
Shaheen Solwa, A. Bamisaye
Evolutionary algorithms (EAs) have recently been applied to Uncoded Space-Time Labeling Diversity (USTLD) systems to produce labeling diversity mappers. However, the most challenging task is choosing the best parameter setting for the EA to create a more ‘optimal’ mapper design. This paper proposes a ‘meta-Genetic Algorithm (GA)’ used to tune hyperparameters for the Labeling Diversity EA. The algorithm is examined on 16, 32 and 64QAM; 32 and 64PSK; 16, 32 and 64APSK and 16APSK constellations that do not show diagonal symmetry. Furthermore, the meta-GA settings and original GA settings are compared in terms of the number of generations taken to converge to a solution. For QAM constellations, the output using the meta-GA settings matched but did not improve with the original settings. However, the number of generations needed to converge to a solution took 120 times less than the number of generations using the original settings. In the 64PSK constellation, a diversity gain of [Formula: see text][Formula: see text]dB was observed while improving on the actual fitness value from 0.0575 to 0.0661. Similarly, with 32APSK constellation, an improvement in fitness value from 0.1457 to 0.1748 was made while showing diversity gains of [Formula: see text][Formula: see text]dB. 64APSK constellation fitness value improved from 0.0708 to 0.0957, and a [Formula: see text][Formula: see text]dB gain was observed. The most significant improvement was made by the asymmetric 16APSK constellation, with gains of [Formula: see text][Formula: see text]dB and increasing its fitness value three times (0.0981 to 0.3000). A study of the effects of optimizing the GA parameters shows that the number of swaps during crossover [Formula: see text] and the radius [Formula: see text] were the two most important variables to optimize when executing this GA.
{"title":"A meta-parameter tuning model to improve the genetic algorithms design of labeling diversity mappers","authors":"Shaheen Solwa, A. Bamisaye","doi":"10.1142/s1793962322500350","DOIUrl":"https://doi.org/10.1142/s1793962322500350","url":null,"abstract":"Evolutionary algorithms (EAs) have recently been applied to Uncoded Space-Time Labeling Diversity (USTLD) systems to produce labeling diversity mappers. However, the most challenging task is choosing the best parameter setting for the EA to create a more ‘optimal’ mapper design. This paper proposes a ‘meta-Genetic Algorithm (GA)’ used to tune hyperparameters for the Labeling Diversity EA. The algorithm is examined on 16, 32 and 64QAM; 32 and 64PSK; 16, 32 and 64APSK and 16APSK constellations that do not show diagonal symmetry. Furthermore, the meta-GA settings and original GA settings are compared in terms of the number of generations taken to converge to a solution. For QAM constellations, the output using the meta-GA settings matched but did not improve with the original settings. However, the number of generations needed to converge to a solution took 120 times less than the number of generations using the original settings. In the 64PSK constellation, a diversity gain of [Formula: see text][Formula: see text]dB was observed while improving on the actual fitness value from 0.0575 to 0.0661. Similarly, with 32APSK constellation, an improvement in fitness value from 0.1457 to 0.1748 was made while showing diversity gains of [Formula: see text][Formula: see text]dB. 64APSK constellation fitness value improved from 0.0708 to 0.0957, and a [Formula: see text][Formula: see text]dB gain was observed. The most significant improvement was made by the asymmetric 16APSK constellation, with gains of [Formula: see text][Formula: see text]dB and increasing its fitness value three times (0.0981 to 0.3000). A study of the effects of optimizing the GA parameters shows that the number of swaps during crossover [Formula: see text] and the radius [Formula: see text] were the two most important variables to optimize when executing this GA.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"42 1","pages":"2250035:1-2250035:16"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85018295","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 : 2021-12-29DOI: 10.1142/s1793962322400013
Longfei Zhou, Lin Zhang
The rapid development of computer vision techniques has brought new opportunities for manufacturing industries, accelerating the intelligence of manufacturing systems in terms of product quality assurance, automatic assembly, and industrial robot control. In the electronics manufacturing industry, intensive variability in component shapes and colors, background brightness, and visual contrast between components and background results in difficulties in printed circuit board image classification. In this paper, we apply computer vision techniques to detect diverse electronic components from their background images, which is a challenging problem in electronics manufacturing industries because there are multiple types of components mounted on the same printed circuit board. Specifically, a 13-layer convolutional neural network (ECON) is proposed to detect electronic components either of a single category or of diverse categories. The proposed network consists of five Convolution-MaxPooling blocks, followed by a flattened layer and two fully connected layers. An electronic component image dataset from a real manufacturing company is applied to compare the performance between ECON, Xception, VGG16, and VGG19. In this dataset, there are 11 categories of components as well as their background images. Results show that ECON has higher accuracy in both single-category and diverse component classification than the other networks.
{"title":"A novel convolutional neural network for electronic component classification with diverse backgrounds","authors":"Longfei Zhou, Lin Zhang","doi":"10.1142/s1793962322400013","DOIUrl":"https://doi.org/10.1142/s1793962322400013","url":null,"abstract":"The rapid development of computer vision techniques has brought new opportunities for manufacturing industries, accelerating the intelligence of manufacturing systems in terms of product quality assurance, automatic assembly, and industrial robot control. In the electronics manufacturing industry, intensive variability in component shapes and colors, background brightness, and visual contrast between components and background results in difficulties in printed circuit board image classification. In this paper, we apply computer vision techniques to detect diverse electronic components from their background images, which is a challenging problem in electronics manufacturing industries because there are multiple types of components mounted on the same printed circuit board. Specifically, a 13-layer convolutional neural network (ECON) is proposed to detect electronic components either of a single category or of diverse categories. The proposed network consists of five Convolution-MaxPooling blocks, followed by a flattened layer and two fully connected layers. An electronic component image dataset from a real manufacturing company is applied to compare the performance between ECON, Xception, VGG16, and VGG19. In this dataset, there are 11 categories of components as well as their background images. Results show that ECON has higher accuracy in both single-category and diverse component classification than the other networks.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"15 1","pages":"2240001:1-2240001:17"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88073763","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 : 2021-12-28DOI: 10.1142/s1793962322500349
T. Mohanraj
The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.
{"title":"Application of AI techniques for modeling the performance measures in milling of 7075-T6 hybrid aluminum metal matrix composites","authors":"T. Mohanraj","doi":"10.1142/s1793962322500349","DOIUrl":"https://doi.org/10.1142/s1793962322500349","url":null,"abstract":"The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"41 1","pages":"2250034:1-2250034:19"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77374466","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 : 2021-12-28DOI: 10.1142/s1793962322500374
C. Maji
In this work, we formulated and analyzed a fractional-order epidemic model of infectious disease (such as SARS, 2019-nCoV and COVID-19) concerning media effect. The model is based on classical susceptible-infected-recovered (SIR) model. Basic properties regarding positivity, boundedness and non-negative solutions are discussed. Basic reproduction number [Formula: see text] of the system has been calculated using next-generation matrix method and it is seen that the disease-free equilibrium is locally as well as globally asymptotically stable if [Formula: see text], otherwise unstable. The existence of endemic equilibrium point is established using the Lambert W function. The condition for global stability has been derived. Numerical simulation suggests that fractional order and media have a large effect on our system dynamics. When media impact is stronger enough, our fractional-order system stabilizes the oscillation.
{"title":"Impact of media coverage on a fractional-order SIR epidemic model","authors":"C. Maji","doi":"10.1142/s1793962322500374","DOIUrl":"https://doi.org/10.1142/s1793962322500374","url":null,"abstract":"In this work, we formulated and analyzed a fractional-order epidemic model of infectious disease (such as SARS, 2019-nCoV and COVID-19) concerning media effect. The model is based on classical susceptible-infected-recovered (SIR) model. Basic properties regarding positivity, boundedness and non-negative solutions are discussed. Basic reproduction number [Formula: see text] of the system has been calculated using next-generation matrix method and it is seen that the disease-free equilibrium is locally as well as globally asymptotically stable if [Formula: see text], otherwise unstable. The existence of endemic equilibrium point is established using the Lambert W function. The condition for global stability has been derived. Numerical simulation suggests that fractional order and media have a large effect on our system dynamics. When media impact is stronger enough, our fractional-order system stabilizes the oscillation.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"36 1","pages":"2250037:1-2250037:17"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78185815","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 : 2021-12-22DOI: 10.1142/s1793962323410015
W. Xu, Jun Meng, S. S. Raja, M. P. Priya
Artificial Intelligence (AI) systems have evolved with digital learning developments to provide thriving soft groups with digital opportunities in response to feedback. When it comes to learning environments, educators’ training feedback is often used as a response recourse. Through the use of final evaluations, students receive feedback that improves their education abilities. To improve academic achievement and explore knowledge in the learning process, this section provides an AI-assisted personalized feedback system (AI-PFS). An individualized feedback system is implemented to learn more about the student’s lack of academic experience interactivity and different collaboration behaviors. According to their benchmark, PFS aims to establish a personalized and reliable feedback process for each class based on their collaborative process and learn analytics modules. It has been proposed to use multi-objective implementations to evaluate students regarding the learning results and teaching methods. With different series of questions sessions for students, AI-PFS has been designed, and the findings showed that it greatly enhances the performance rate of 95.32% with personalized and reasonable predictive.
{"title":"Artificial intelligence in constructing personalized and accurate feedback systems for students","authors":"W. Xu, Jun Meng, S. S. Raja, M. P. Priya","doi":"10.1142/s1793962323410015","DOIUrl":"https://doi.org/10.1142/s1793962323410015","url":null,"abstract":"Artificial Intelligence (AI) systems have evolved with digital learning developments to provide thriving soft groups with digital opportunities in response to feedback. When it comes to learning environments, educators’ training feedback is often used as a response recourse. Through the use of final evaluations, students receive feedback that improves their education abilities. To improve academic achievement and explore knowledge in the learning process, this section provides an AI-assisted personalized feedback system (AI-PFS). An individualized feedback system is implemented to learn more about the student’s lack of academic experience interactivity and different collaboration behaviors. According to their benchmark, PFS aims to establish a personalized and reliable feedback process for each class based on their collaborative process and learn analytics modules. It has been proposed to use multi-objective implementations to evaluate students regarding the learning results and teaching methods. With different series of questions sessions for students, AI-PFS has been designed, and the findings showed that it greatly enhances the performance rate of 95.32% with personalized and reasonable predictive.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"9 1","pages":"2341001:1-2341001:21"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79915532","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 : 2021-12-11DOI: 10.1142/s1793962322500295
N. Ullah, Alsharef Mohammad
The coupled tank system is the most widely used sub-component in chemical process industries. Fluid mixing is a major step in chemical processes that alters the material properties and cost. Fluid flow and its level regulation between several tanks are important control problems. As the first step, this paper addresses the level regulation problem using classical integer order proportional, derivative, integral (PID), fractional order PID controllers. As a second step, model-based robust fractional order controllers are derived using sliding mode approach in order to achieve the desired response, parameters of the proposed controllers are tuned using genetic algorithm. Finally, system performance under all variants of control schemes has been tested using numerical simulations.
{"title":"A simulation approach for closed-loop control of coupled four tank system","authors":"N. Ullah, Alsharef Mohammad","doi":"10.1142/s1793962322500295","DOIUrl":"https://doi.org/10.1142/s1793962322500295","url":null,"abstract":"The coupled tank system is the most widely used sub-component in chemical process industries. Fluid mixing is a major step in chemical processes that alters the material properties and cost. Fluid flow and its level regulation between several tanks are important control problems. As the first step, this paper addresses the level regulation problem using classical integer order proportional, derivative, integral (PID), fractional order PID controllers. As a second step, model-based robust fractional order controllers are derived using sliding mode approach in order to achieve the desired response, parameters of the proposed controllers are tuned using genetic algorithm. Finally, system performance under all variants of control schemes has been tested using numerical simulations.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"14 1","pages":"2250029:1-2250029:17"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87722514","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 : 2021-12-09DOI: 10.1142/s1793962323410039
Songyan Zhang, Chaoyong Hu
To estimate the parameters of the model of option pricing based on the model of rough fractional stochastic volatility (RFSV), we have carried out the empirical analysis during our study on the pricing of SSE 50ETF options in China. First, we have estimated the parameters of option pricing model by adopting the Monte Carlo simulation. Subsequently, we have empirically examined the pricing performance of the RFSV model by adopting the SSE 50ETF option price from January 2019 to December 2020. Our research findings indicate that by leveraging the RFSV model, we are able to attain a more accurate and stable level of option pricing than the conventional Black–Scholes (B-S) model on constant volatility. The errors of option pricing incurred by the B-S model proved to be larger and exhibited higher volatility, revealing the significant impact imposed by stochastic volatility on option pricing.
{"title":"Empirical study based on the model of rough fractional stochastic volatility (RFSV)","authors":"Songyan Zhang, Chaoyong Hu","doi":"10.1142/s1793962323410039","DOIUrl":"https://doi.org/10.1142/s1793962323410039","url":null,"abstract":"To estimate the parameters of the model of option pricing based on the model of rough fractional stochastic volatility (RFSV), we have carried out the empirical analysis during our study on the pricing of SSE 50ETF options in China. First, we have estimated the parameters of option pricing model by adopting the Monte Carlo simulation. Subsequently, we have empirically examined the pricing performance of the RFSV model by adopting the SSE 50ETF option price from January 2019 to December 2020. Our research findings indicate that by leveraging the RFSV model, we are able to attain a more accurate and stable level of option pricing than the conventional Black–Scholes (B-S) model on constant volatility. The errors of option pricing incurred by the B-S model proved to be larger and exhibited higher volatility, revealing the significant impact imposed by stochastic volatility on option pricing.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"26 1","pages":"2341003:1-2341003:10"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90210226","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 : 2021-12-06DOI: 10.1142/s1793962322500283
S. Djilali, Soufiane Bentout, S. Sushanth Kumar, T. Touaoula
In this research, we are interested in discussing the evolution of the COVID-19 infection cases and predicting the spread of COVID-19 disease in Algeria and India. To this aim, we will approximate the transmission rate in terms of the measures taken by the governments. The least square method is used with an accuracy of 95% for fitting the artificial solution with the real data declared by WHO for the purpose of approximating the density of asymptomatic individuals for COVID-19 disease. As a result, we obtained the different values of the basic reproduction number (BRN) corresponding to each measure taken by the governments. Moreover, we estimate the number of asymptomatic infected persons at the epidemic peak for each country. Further, we will determine the needed ICU beds (intense medical carte beds) and regular treatment beds. Also, we provide the outcome of governmental strategies in reducing the spread of disease. Combining all these components, we offer some suggestions about the necessity of using the recently discovered vaccines as Pfizer/Bioentec and Moderna for limiting the spread of the COVID-19 disease in the studied countries.
{"title":"Approximating the asymptomatic infectious cases of the COVID-19 disease in Algeria and India using a mathematical model","authors":"S. Djilali, Soufiane Bentout, S. Sushanth Kumar, T. Touaoula","doi":"10.1142/s1793962322500283","DOIUrl":"https://doi.org/10.1142/s1793962322500283","url":null,"abstract":"In this research, we are interested in discussing the evolution of the COVID-19 infection cases and predicting the spread of COVID-19 disease in Algeria and India. To this aim, we will approximate the transmission rate in terms of the measures taken by the governments. The least square method is used with an accuracy of 95% for fitting the artificial solution with the real data declared by WHO for the purpose of approximating the density of asymptomatic individuals for COVID-19 disease. As a result, we obtained the different values of the basic reproduction number (BRN) corresponding to each measure taken by the governments. Moreover, we estimate the number of asymptomatic infected persons at the epidemic peak for each country. Further, we will determine the needed ICU beds (intense medical carte beds) and regular treatment beds. Also, we provide the outcome of governmental strategies in reducing the spread of disease. Combining all these components, we offer some suggestions about the necessity of using the recently discovered vaccines as Pfizer/Bioentec and Moderna for limiting the spread of the COVID-19 disease in the studied countries.","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"189 1","pages":"2250028:1-2250028:18"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73476170","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 : 2021-11-29DOI: 10.1142/s1793962323410040
Xiaoxu Qi, Yaling Zhang, Sheng Cao, S. Yan, Hongbang Su
{"title":"Human-computer interaction based on the intelligent information retrieval method for customer satisfaction in power system service","authors":"Xiaoxu Qi, Yaling Zhang, Sheng Cao, S. Yan, Hongbang Su","doi":"10.1142/s1793962323410040","DOIUrl":"https://doi.org/10.1142/s1793962323410040","url":null,"abstract":"","PeriodicalId":13657,"journal":{"name":"Int. J. Model. Simul. Sci. Comput.","volume":"90 1","pages":"2341004:1-2341004:25"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83547853","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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