Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as dev...Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.展开更多
This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical ...This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.展开更多
The design of acoustic models is of vital importance to build a reliable connection between acoustic wave-form and linguistic messages in terms of individual speech units. According to the characteristic of Chinese ph...The design of acoustic models is of vital importance to build a reliable connection between acoustic wave-form and linguistic messages in terms of individual speech units. According to the characteristic of Chinese phonemes, the base acoustic phoneme units set is decided and refined and a decision tree based state tying approach is explored. Since one of the advantages of top-down tying method is flexibility in maintaining a balance between model accuracy and complexity, relevant adjustments are conducted, such as the stopping criterion of decision tree node splitting, during which optimal thresholds are captured. Better results are achieved in improving acoustic modeling accuracy as well as minimizing the scale of the model to a trainable extent.展开更多
Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and ...Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and using one classification algorithm to reach good results is difficult.For this reason,we use a combination of classification techniques to reach an efficient and accurate classification model.This model combination is called the Ensemble model.We need to predict new medical data with a high accuracy value in a small processing time.We propose a new ensemble model MDRL which is efficient with different datasets.The MDRL gives the highest accuracy value.It saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in parallel.We implement five different algorithms on five variant datasets which are Heart Disease,Health General,Diabetes,Heart Attack,and Covid-19 Datasets.The four algorithms are Random Forest(RF),Decision Tree(DT),Logistic Regression(LR),and Multi-layer Perceptron(MLP).In addition to MDRL(our proposed ensemble model)which includes MLP,DT,RF,and LR together.From our experiments,we conclude that our ensemble model has the best accuracy value for most datasets.We reach that the combination of the Correlation Feature Selection(CFS)algorithm and our ensemble model is the best for giving the highest accuracy value.The accuracy values for our ensemble model based on CFS are 98.86,97.96,100,99.33,and 99.37 for heart disease,health general,Covid-19,heart attack,and diabetes datasets respectively.展开更多
文摘Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.
文摘This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.
基金Project 60475007 supported by the National Natural Science Foundation of China
文摘The design of acoustic models is of vital importance to build a reliable connection between acoustic wave-form and linguistic messages in terms of individual speech units. According to the characteristic of Chinese phonemes, the base acoustic phoneme units set is decided and refined and a decision tree based state tying approach is explored. Since one of the advantages of top-down tying method is flexibility in maintaining a balance between model accuracy and complexity, relevant adjustments are conducted, such as the stopping criterion of decision tree node splitting, during which optimal thresholds are captured. Better results are achieved in improving acoustic modeling accuracy as well as minimizing the scale of the model to a trainable extent.
文摘Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and using one classification algorithm to reach good results is difficult.For this reason,we use a combination of classification techniques to reach an efficient and accurate classification model.This model combination is called the Ensemble model.We need to predict new medical data with a high accuracy value in a small processing time.We propose a new ensemble model MDRL which is efficient with different datasets.The MDRL gives the highest accuracy value.It saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in parallel.We implement five different algorithms on five variant datasets which are Heart Disease,Health General,Diabetes,Heart Attack,and Covid-19 Datasets.The four algorithms are Random Forest(RF),Decision Tree(DT),Logistic Regression(LR),and Multi-layer Perceptron(MLP).In addition to MDRL(our proposed ensemble model)which includes MLP,DT,RF,and LR together.From our experiments,we conclude that our ensemble model has the best accuracy value for most datasets.We reach that the combination of the Correlation Feature Selection(CFS)algorithm and our ensemble model is the best for giving the highest accuracy value.The accuracy values for our ensemble model based on CFS are 98.86,97.96,100,99.33,and 99.37 for heart disease,health general,Covid-19,heart attack,and diabetes datasets respectively.
