[ Objective] The research aimed to study response rule of the M. aeruginosa fluorescence on the biological toxicity of HgCI2. [ Method ] M. aeruginosa as material, fluorescence intensity at its best excitation and emi...[ Objective] The research aimed to study response rule of the M. aeruginosa fluorescence on the biological toxicity of HgCI2. [ Method ] M. aeruginosa as material, fluorescence intensity at its best excitation and emission wavelengths as measured indicator, influence of the HgCI2 at different mass concentrations on fluorescence intensity of the M. aeruginosa was discussed initially. [ Result] HgCI2 at different mass concentrations had different influences on M. aeruginosa. HgCI2 at low concentration （0.002 -0.004 mg/L）could promote photosynthesis of the M. aeruginosa. It showed as fluorescence value of the algae liquid becoming smaller. 0.010 -0.400 mg/L of HgCI2 inhibited photosynthesis of the M. aeruginosa. It showed as fluorescence value of the algae liquid becoming bigger. Moreover, inhibition effect increased as HgCI2 concentration rose, showing a positive correlation between HgCI2 concentration and toxicity （ R 2 = 0.963 5 ）. [ Conclusion ] The research provided new theoretical basis for quickly measuring water toxicity.展开更多
The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The determination of the value of this variable when in...The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The determination of the value of this variable when initiating software projects allows us to plan adequately any forthcoming activities. As far as estimation and prediction is concerned there is still a number of unsolved problems and errors. To obtain good results it is essential to take into consideration any previous projects. Estimating the effort with a high grade of reliability is a problem which has not yet been solved and even the project manager has to deal with it since the beginning. In this study, performance of M5-Rules Algorithm, single conjunctive rule learner and decision table majority classifier are experimented for modeling of Effort Estimation of Software Projects and performance of developed models is compared with the existing algorithms namely Halstead, Walston-Felix, Bailey-Basili, Doty in terms of MAE and RMSE. The proposed techniques are run in the WEKA environment for building the model structure for software effort and the formulae of existing models are calculated in the MATLAB environment. The performance evaluation criteria are based on MAE and RMSE. The result shows that the M5-Rules have the best performance and can be used for the effort estimation of all types of software projects.展开更多
为更好地进行网络管理和网络安全维护,通过研究加密流量的内容统计特征,提出基于M-序列检验的网络数据随机性评估算法(network data randomness estimation,NDRE)以识别加密流量。采用M-序列检验方法对序列随机性进行量化;根据负载序列...为更好地进行网络管理和网络安全维护,通过研究加密流量的内容统计特征,提出基于M-序列检验的网络数据随机性评估算法(network data randomness estimation,NDRE)以识别加密流量。采用M-序列检验方法对序列随机性进行量化;根据负载序列长度,自适应训练得到最优化参数集;利用最小风险贝叶斯准则,对加密流量进行识别。实验结果表明,与基于熵的方法相比,在控制一定计算复杂度的情况下,NDRE精确度有较大提高。展开更多
<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estima...<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.</span> </div>展开更多
基金Supported by National 863 Item,China (2007AA092201)
文摘[ Objective] The research aimed to study response rule of the M. aeruginosa fluorescence on the biological toxicity of HgCI2. [ Method ] M. aeruginosa as material, fluorescence intensity at its best excitation and emission wavelengths as measured indicator, influence of the HgCI2 at different mass concentrations on fluorescence intensity of the M. aeruginosa was discussed initially. [ Result] HgCI2 at different mass concentrations had different influences on M. aeruginosa. HgCI2 at low concentration （0.002 -0.004 mg/L）could promote photosynthesis of the M. aeruginosa. It showed as fluorescence value of the algae liquid becoming smaller. 0.010 -0.400 mg/L of HgCI2 inhibited photosynthesis of the M. aeruginosa. It showed as fluorescence value of the algae liquid becoming bigger. Moreover, inhibition effect increased as HgCI2 concentration rose, showing a positive correlation between HgCI2 concentration and toxicity （ R 2 = 0.963 5 ）. [ Conclusion ] The research provided new theoretical basis for quickly measuring water toxicity.
文摘The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The determination of the value of this variable when initiating software projects allows us to plan adequately any forthcoming activities. As far as estimation and prediction is concerned there is still a number of unsolved problems and errors. To obtain good results it is essential to take into consideration any previous projects. Estimating the effort with a high grade of reliability is a problem which has not yet been solved and even the project manager has to deal with it since the beginning. In this study, performance of M5-Rules Algorithm, single conjunctive rule learner and decision table majority classifier are experimented for modeling of Effort Estimation of Software Projects and performance of developed models is compared with the existing algorithms namely Halstead, Walston-Felix, Bailey-Basili, Doty in terms of MAE and RMSE. The proposed techniques are run in the WEKA environment for building the model structure for software effort and the formulae of existing models are calculated in the MATLAB environment. The performance evaluation criteria are based on MAE and RMSE. The result shows that the M5-Rules have the best performance and can be used for the effort estimation of all types of software projects.
文摘为更好地进行网络管理和网络安全维护,通过研究加密流量的内容统计特征,提出基于M-序列检验的网络数据随机性评估算法(network data randomness estimation,NDRE)以识别加密流量。采用M-序列检验方法对序列随机性进行量化;根据负载序列长度,自适应训练得到最优化参数集;利用最小风险贝叶斯准则,对加密流量进行识别。实验结果表明,与基于熵的方法相比,在控制一定计算复杂度的情况下,NDRE精确度有较大提高。
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.</span> </div>
