Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19)...Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.展开更多
Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)...Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.展开更多
Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such challenges.Metaheuristic alg...Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such challenges.Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure.Sine Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&Cosine.However,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal regions.In this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive weight.The first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching capabilities.To evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are used.Moreover,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of SCA.The results show that RDSCA has competitive results with other metaheuristics algorithms.展开更多
Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning thi...Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.展开更多
基金The authors thank the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number(120/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4331004DSR06).
文摘Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.
基金supported by the NationalNatural Science Foundation of China(No.11672098).
文摘Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.
基金supported in part by the Hangzhou Science and Technology Development Plan Project(Grant No.20191203B30).
文摘Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such challenges.Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure.Sine Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&Cosine.However,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal regions.In this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive weight.The first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching capabilities.To evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are used.Moreover,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of SCA.The results show that RDSCA has competitive results with other metaheuristics algorithms.
基金supported in part by the National Natural Science Foundation of China under Grants No.62027803,No.61601096,No.61971111,and No.61801089in part by the Science and Technology Program under Grants No.8091C24,No.2021JCJQJJ0949,and No.2022JCJQJJ0784in part by the Industrial Technology Development Program under Grant No.2020110C041.
文摘Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.
