This article discusses a multiple hierarchy decision analysis in which the Analytic Hierarchy Process and Delphi method were combined with practice of funds management so as to obtain a scientific management system. T...This article discusses a multiple hierarchy decision analysis in which the Analytic Hierarchy Process and Delphi method were combined with practice of funds management so as to obtain a scientific management system. The design procedures and a practical example have been given to verify the feasibility. The work was supported by China National Natural Science Foundation (NNSF), and the introduced procedures have been used in some departments of the NNSF.展开更多
In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integr...In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.展开更多
In this paper, we suggest a deep learning strategy for decision support, based on a greedy algorithm. Decision making support by artificial intelligence is of the most challenging trends in modern computer science. Cu...In this paper, we suggest a deep learning strategy for decision support, based on a greedy algorithm. Decision making support by artificial intelligence is of the most challenging trends in modern computer science. Currently various strategies exist and are increasingly improved in order to meet practical needs of user-oriented platforms like Microsoft, Google, Amazon, etc.展开更多
With the increase of science popularization, evaluation of science popularization has become an urgent demand. Considering science popularization bases as independent agents, a self-determined evaluation approach for ...With the increase of science popularization, evaluation of science popularization has become an urgent demand. Considering science popularization bases as independent agents, a self-determined evaluation approach for science popularization using induced ordered weighted averaging (IOWA) operator and particle swarm optimization (PSO) is proposed in this paper.Firstly, six factors including science popularization personnel, space, fund,media, activity and influence are selected to construct an index system for science popularization evaluation. On this basis, the absolute dominance and relative dominance of evaluation indexes are used as induced components, and the prior order of the evaluation indexes is determined. Besides, the optimization model of index weighted vectors is established by IOWA operator, index weighted vectors are calculated by particle swarm optimization algorithm, and index weighted vectors and evaluation value vectors are obtain. Finally, the optimal evaluation vectors and evaluation results are given according to the Perron-Frobenius decision eigenvalve theorem .展开更多
At the first gathering of its kind on the role of science in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030,over 750 scientists,policymakers,business people,and practitioners met in Geneva fr...At the first gathering of its kind on the role of science in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030,over 750 scientists,policymakers,business people,and practitioners met in Geneva from January 27–29,2016.The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 fea-展开更多
文摘This article discusses a multiple hierarchy decision analysis in which the Analytic Hierarchy Process and Delphi method were combined with practice of funds management so as to obtain a scientific management system. The design procedures and a practical example have been given to verify the feasibility. The work was supported by China National Natural Science Foundation (NNSF), and the introduced procedures have been used in some departments of the NNSF.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/147/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Path of Research Funding Program.
文摘In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.
文摘In this paper, we suggest a deep learning strategy for decision support, based on a greedy algorithm. Decision making support by artificial intelligence is of the most challenging trends in modern computer science. Currently various strategies exist and are increasingly improved in order to meet practical needs of user-oriented platforms like Microsoft, Google, Amazon, etc.
文摘With the increase of science popularization, evaluation of science popularization has become an urgent demand. Considering science popularization bases as independent agents, a self-determined evaluation approach for science popularization using induced ordered weighted averaging (IOWA) operator and particle swarm optimization (PSO) is proposed in this paper.Firstly, six factors including science popularization personnel, space, fund,media, activity and influence are selected to construct an index system for science popularization evaluation. On this basis, the absolute dominance and relative dominance of evaluation indexes are used as induced components, and the prior order of the evaluation indexes is determined. Besides, the optimization model of index weighted vectors is established by IOWA operator, index weighted vectors are calculated by particle swarm optimization algorithm, and index weighted vectors and evaluation value vectors are obtain. Finally, the optimal evaluation vectors and evaluation results are given according to the Perron-Frobenius decision eigenvalve theorem .
文摘At the first gathering of its kind on the role of science in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030,over 750 scientists,policymakers,business people,and practitioners met in Geneva from January 27–29,2016.The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 fea-
