This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In ...This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.展开更多
The article introduces ZTE's Softswitch-based NGN solutions. such as the long distance VolP service solution over data back- bone,and local voice and data service solution over MAN.Three application cases are anal...The article introduces ZTE's Softswitch-based NGN solutions. such as the long distance VolP service solution over data back- bone,and local voice and data service solution over MAN.Three application cases are analyzed,and NGN's features in aspects of network architecture,service provision and network management are summarized.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
目的解决当前中医医案命名实体识别模型识别效果欠佳的问题,提升中医医案结构化的效率与精准度。方法构建融合多维度信息的BDM-CRF命名实体识别神经网络模型,融合双向长短期记忆网络模型(Bidirectional long short-term memory,BiLSTM)...目的解决当前中医医案命名实体识别模型识别效果欠佳的问题,提升中医医案结构化的效率与精准度。方法构建融合多维度信息的BDM-CRF命名实体识别神经网络模型,融合双向长短期记忆网络模型(Bidirectional long short-term memory,BiLSTM)、门控空洞卷积神经网络模型(Gated-dilated convolutional neural networks,DGCNN)和多头注意力机制模型(Multi-head attention,MHA)进行多维度特征抽取,通过条件随机场(Conditional random field,CRF)模型对特征抽取结果进行约束输出;通过对比实验、消融实验、预训练模型对比实验及神经网络结构对比实验验证该模型的有效性,通过个案分析验证并展示模型的提取效果。结果本研究提出的模型比对照模型精确率平均提升2.30%、召回率平均提升4.73%、F1值平均提升3.53%。个案分析结果表明,本研究模型能正确识别出中医医案文本中的大部分实体内容。结论本研究提出的融合多维度信息的中医医案命名实体识别模型能取得更佳的识别效果,可有效提升中医医案结构化的精准度及效率,促进中医医案处理智能化。展开更多
In software industry the major problem encountered during project scheduling is in deciding what proportion of the resources has allocated to the testing phase. In general it has been observed that about 40%-50% of th...In software industry the major problem encountered during project scheduling is in deciding what proportion of the resources has allocated to the testing phase. In general it has been observed that about 40%-50% of the resources need to be allocated to the testing phase. However it is very difficult to predict the exact amount of effort required to be allocated to testing phase. As a result the project planning goes haywire. The project which has not been tested sufficiently can cause huge losses to the organization. This research paper focuses on finding a method which gives a measure of the effort to be spent on the testing phase. This paper provides effort estimates during pre-coding and post-coding phases using neural network to predict more accurately.展开更多
To evaluate transmission rate of highly dynamic space networks,a new method for studying space network capacity is proposed in this paper. Using graph theory,network capacity is defined as the maximum amount of flows ...To evaluate transmission rate of highly dynamic space networks,a new method for studying space network capacity is proposed in this paper. Using graph theory,network capacity is defined as the maximum amount of flows ground stations can receive per unit time. Combined with a hybrid constellation model,network capacity is calculated and further analyzed for practical cases. Simulation results show that network capacity will increase to different extents as link capacity,minimum ground elevation constraint and satellite onboard processing capability change. Considering the efficiency and reliability of communication networks,how to scientifically design satellite networks is also discussed.展开更多
基金Supported by the National Science of China(6 0 0 75 0 15 ) and Key Project of Scientific and Technological Departmentin Anhui
文摘This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.
文摘The article introduces ZTE's Softswitch-based NGN solutions. such as the long distance VolP service solution over data back- bone,and local voice and data service solution over MAN.Three application cases are analyzed,and NGN's features in aspects of network architecture,service provision and network management are summarized.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
文摘目的解决当前中医医案命名实体识别模型识别效果欠佳的问题,提升中医医案结构化的效率与精准度。方法构建融合多维度信息的BDM-CRF命名实体识别神经网络模型,融合双向长短期记忆网络模型(Bidirectional long short-term memory,BiLSTM)、门控空洞卷积神经网络模型(Gated-dilated convolutional neural networks,DGCNN)和多头注意力机制模型(Multi-head attention,MHA)进行多维度特征抽取,通过条件随机场(Conditional random field,CRF)模型对特征抽取结果进行约束输出;通过对比实验、消融实验、预训练模型对比实验及神经网络结构对比实验验证该模型的有效性,通过个案分析验证并展示模型的提取效果。结果本研究提出的模型比对照模型精确率平均提升2.30%、召回率平均提升4.73%、F1值平均提升3.53%。个案分析结果表明,本研究模型能正确识别出中医医案文本中的大部分实体内容。结论本研究提出的融合多维度信息的中医医案命名实体识别模型能取得更佳的识别效果,可有效提升中医医案结构化的精准度及效率,促进中医医案处理智能化。
文摘In software industry the major problem encountered during project scheduling is in deciding what proportion of the resources has allocated to the testing phase. In general it has been observed that about 40%-50% of the resources need to be allocated to the testing phase. However it is very difficult to predict the exact amount of effort required to be allocated to testing phase. As a result the project planning goes haywire. The project which has not been tested sufficiently can cause huge losses to the organization. This research paper focuses on finding a method which gives a measure of the effort to be spent on the testing phase. This paper provides effort estimates during pre-coding and post-coding phases using neural network to predict more accurately.
基金Sponsored by the National Natural Science Foundation of China(Grant No.6137110061001093+6 种基金61401118)the Natural Science Foundation of Shandong Province(Grant No.ZR2014FP016)the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology(Grant No.HIT.NSRIF.2011114HIT.NSRIF.2013136HIT.NSRIF.2016100)the Scientific Research Foundation of Harbin Institute of Technology at Weihai(Grant No.HIT(WH)201409HIT(WH)201410)
文摘To evaluate transmission rate of highly dynamic space networks,a new method for studying space network capacity is proposed in this paper. Using graph theory,network capacity is defined as the maximum amount of flows ground stations can receive per unit time. Combined with a hybrid constellation model,network capacity is calculated and further analyzed for practical cases. Simulation results show that network capacity will increase to different extents as link capacity,minimum ground elevation constraint and satellite onboard processing capability change. Considering the efficiency and reliability of communication networks,how to scientifically design satellite networks is also discussed.
