By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the li...By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.展开更多
评审专家遴选是技术评审中的关键环节。鉴于颠覆性技术专家预判平台预判系统对时效性和智能型的要求,专家遴选对预判结果具有决定性影响。通过学术专长匹配和专业遴选来选择符合要求的专家,可以降低成本,提高推荐效率与准确度,完成颠覆...评审专家遴选是技术评审中的关键环节。鉴于颠覆性技术专家预判平台预判系统对时效性和智能型的要求,专家遴选对预判结果具有决定性影响。通过学术专长匹配和专业遴选来选择符合要求的专家,可以降低成本,提高推荐效率与准确度,完成颠覆性技术的预测任务。基于学术网络表示学习的方法既可以避免大量特征工程,又可以方便不同类型的特征进行融合。利用异质网络表示学习方法和标签排序的学术专长画像方法构建专家库,并使用融合专家综合评价指标特征的匹配方法对待预判的颠覆性技术和专家专长进行匹配,为专家遴选提供一份专业背景匹配的候选专家列表。这种方法在Academic Social Network数据集上进行模拟实验。实验结果表明,这种方法能提升项目评审专家学术专长匹配,在加入综合指标特征后,专家的综合指标特征能有效地反馈到实验结果中,从而提高评审系统的时效性和智能性。展开更多
Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this pap...Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.展开更多
We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic informa...We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic information for applications. Facing this challenge, a new tactic, Human-Computer Collaborative (HCC) tactic, and a corresponding new method, Operator-Object Directed (OOD) method, are proposed for the design of a system for feature extraction from large scale aerial images. We hold that in almost all technical complex systems, full automation will be neither technically feasible nor socially acceptable. The system should be designed to optimize through the cooperative operation with two agents in the system: the hurtan and the computer.展开更多
基金The authors would like to acknowledge the financial support from the National Natural Science Foundation of China(No.61379145)the Joint Funds of CETC(Grant No.20166141B08020101).
文摘By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.
文摘评审专家遴选是技术评审中的关键环节。鉴于颠覆性技术专家预判平台预判系统对时效性和智能型的要求,专家遴选对预判结果具有决定性影响。通过学术专长匹配和专业遴选来选择符合要求的专家,可以降低成本,提高推荐效率与准确度,完成颠覆性技术的预测任务。基于学术网络表示学习的方法既可以避免大量特征工程,又可以方便不同类型的特征进行融合。利用异质网络表示学习方法和标签排序的学术专长画像方法构建专家库,并使用融合专家综合评价指标特征的匹配方法对待预判的颠覆性技术和专家专长进行匹配,为专家遴选提供一份专业背景匹配的候选专家列表。这种方法在Academic Social Network数据集上进行模拟实验。实验结果表明,这种方法能提升项目评审专家学术专长匹配,在加入综合指标特征后,专家的综合指标特征能有效地反馈到实验结果中,从而提高评审系统的时效性和智能性。
基金Supported in part by the National Natural Science Foundation of China (No.60272046, No.60102011), Na-tional High Technology Project of China (No.2002AA143010), Natural Science Foundation of Jiangsu Province (No.BK2001042), and the Foundation for Excellent Doctoral Dissertation of Southeast Univer-sity (No.YBJJ0412).
文摘Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.
文摘We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic information for applications. Facing this challenge, a new tactic, Human-Computer Collaborative (HCC) tactic, and a corresponding new method, Operator-Object Directed (OOD) method, are proposed for the design of a system for feature extraction from large scale aerial images. We hold that in almost all technical complex systems, full automation will be neither technically feasible nor socially acceptable. The system should be designed to optimize through the cooperative operation with two agents in the system: the hurtan and the computer.
