People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experien...People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic.展开更多
The convergence results of block iterative schemes from the EG （Explicit Group） family have been shown to be one of efficient iterative methods in solving any linear systems generated from approximation equations. A...The convergence results of block iterative schemes from the EG （Explicit Group） family have been shown to be one of efficient iterative methods in solving any linear systems generated from approximation equations. Apart from block iterative methods, the formulation of the MSOR （Modified Successive Over-Relaxation） method known as SOR method with red-black ordering strategy by using two accelerated parameters, ω and ω′, has also improved the convergence rate of the standard SOR method. Due to the effectiveness of these iterative methods, the primary goal of this paper is to examine the performance of the EG family without or with accelerated parameters in solving second order two-point nonlinear boundary value problems. In this work, the second order two-point nonlinear boundary value problems need to be discretized by using the second order central difference scheme in constructing a nonlinear finite difference approximation equation. Then this approximation equation leads to a nonlinear system. As well known that to linearize nonlinear systems, the Newton method has been proposed to transform the original system into the form of linear system. In addition to that, the basic formulation and implementation of 2 and 4-point EG iterative methods based on GS （Gauss-Seidel）, SOR and MSOR approaches, namely EGGS, EGSOR and EGMSOR respectively are also presented. Then, combinations between the EG family and Newton scheme are indicated as EGGS-Newton, EGSOR-Newton and EGMSOR-Newton methods respectively. For comparison purpose, several numerical experiments of three problems are conducted in examining the effectiveness of tested methods. Finally, it can be concluded that the 4-point EGMSOR-Newton method is more superior in accelerating the convergence rate compared with the tested methods.展开更多
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work(Project Number UB-2-1442).
文摘People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic.
文摘The convergence results of block iterative schemes from the EG （Explicit Group） family have been shown to be one of efficient iterative methods in solving any linear systems generated from approximation equations. Apart from block iterative methods, the formulation of the MSOR （Modified Successive Over-Relaxation） method known as SOR method with red-black ordering strategy by using two accelerated parameters, ω and ω′, has also improved the convergence rate of the standard SOR method. Due to the effectiveness of these iterative methods, the primary goal of this paper is to examine the performance of the EG family without or with accelerated parameters in solving second order two-point nonlinear boundary value problems. In this work, the second order two-point nonlinear boundary value problems need to be discretized by using the second order central difference scheme in constructing a nonlinear finite difference approximation equation. Then this approximation equation leads to a nonlinear system. As well known that to linearize nonlinear systems, the Newton method has been proposed to transform the original system into the form of linear system. In addition to that, the basic formulation and implementation of 2 and 4-point EG iterative methods based on GS （Gauss-Seidel）, SOR and MSOR approaches, namely EGGS, EGSOR and EGMSOR respectively are also presented. Then, combinations between the EG family and Newton scheme are indicated as EGGS-Newton, EGSOR-Newton and EGMSOR-Newton methods respectively. For comparison purpose, several numerical experiments of three problems are conducted in examining the effectiveness of tested methods. Finally, it can be concluded that the 4-point EGMSOR-Newton method is more superior in accelerating the convergence rate compared with the tested methods.
