In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,sp...In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.展开更多
Large amounts of documents are exchanged during the construction phase of projects, which covers the important management information. To utilize the exchanged documents to support decisionmaking of the management sta...Large amounts of documents are exchanged during the construction phase of projects, which covers the important management information. To utilize the exchanged documents to support decisionmaking of the management staffs, the requirement analysis was carried out based on the interviews to the practitioners. A decision support system called Explyzer+ was developed based on the previous prototype system Explyzer. The latter was enhanced by adding the functions to automate the whole process and the techniques of data mining including decision tree analysis and clustering analysis. A case study for in-depth information analysis was conducted based on the data obtained from a large construction project to demonstrate its feasibility and effectiveness. The system could effectively assist the management staffs to carry out in-depth information analysis for decision-making in construction projects.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 technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibi...The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.展开更多
将Tapdata Data Services新一代实时数据处理装置作为电力营销智能决策支持系统的硬件,在软件设计阶段,引入数据挖掘算法分析波峰和波谷阶段的电力资源交易总量与供电输出之间的关系,波峰和波谷阶段的电力资源交易总量与供电输出差值与...将Tapdata Data Services新一代实时数据处理装置作为电力营销智能决策支持系统的硬件,在软件设计阶段,引入数据挖掘算法分析波峰和波谷阶段的电力资源交易总量与供电输出之间的关系,波峰和波谷阶段的电力资源交易总量与供电输出差值与电力营销安全阈值范围之间的差值,结合电价对于用电负荷的调节能力制定电力营销决策。测试结果表明,设计系统的每秒冲突次数控制在0.20次以内,具有较高的稳定性,且其应用后售电量最低为4.585×10^(13)kW·h,优于对比系统,应用效果更佳。展开更多
基金Supported by National"863"High-tech Project(2006AA10A309)Jilin Technology Gallery Key Project(20060213)~~
文摘In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.
基金the National Technological Promotion Program for the 10th-Five-Year Plan of China (No. 2004BA209B04)
文摘Large amounts of documents are exchanged during the construction phase of projects, which covers the important management information. To utilize the exchanged documents to support decisionmaking of the management staffs, the requirement analysis was carried out based on the interviews to the practitioners. A decision support system called Explyzer+ was developed based on the previous prototype system Explyzer. The latter was enhanced by adding the functions to automate the whole process and the techniques of data mining including decision tree analysis and clustering analysis. A case study for in-depth information analysis was conducted based on the data obtained from a large construction project to demonstrate its feasibility and effectiveness. The system could effectively assist the management staffs to carry out in-depth information analysis for decision-making in construction projects.
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.
文摘The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.
文摘将Tapdata Data Services新一代实时数据处理装置作为电力营销智能决策支持系统的硬件,在软件设计阶段,引入数据挖掘算法分析波峰和波谷阶段的电力资源交易总量与供电输出之间的关系,波峰和波谷阶段的电力资源交易总量与供电输出差值与电力营销安全阈值范围之间的差值,结合电价对于用电负荷的调节能力制定电力营销决策。测试结果表明,设计系统的每秒冲突次数控制在0.20次以内,具有较高的稳定性,且其应用后售电量最低为4.585×10^(13)kW·h,优于对比系统,应用效果更佳。
