The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale...The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale or other inappropriate purposes.Similarly,national and international organizations have country-level and company-level private information that could be accessed by different network attacks.Therefore,the need for a Network Intruder Detection System(NIDS)becomes essential for protecting these networks and organizations.In the evolution of NIDS,Artificial Intelligence(AI)assisted tools and methods have been widely adopted to provide effective solutions.However,the development of NIDS still faces challenges at the dataset and machine learning levels,such as large deviations in numeric features,the presence of numerous irrelevant categorical features resulting in reduced cardinality,and class imbalance in multiclass-level data.To address these challenges and offer a unified solution to NIDS development,this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment.Cardinality reduction was applied to categorical features through the binning method.Subsequently,the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method.Finally,the preprocessed,refined,and oversampled feature set was divided into training and test sets with an 80–20 ratio,and two experiments were conducted.In Experiment 1,the binary classification was executed using four machine learning classifiers,with the extra trees classifier achieving the highest accuracy of 97.23%and an AUC of 0.9961.In Experiment 2,multiclass classification was performed,and the extra trees classifier emerged as the most effective,achieving an accuracy of 81.27%and an AUC of 0.97.The results were evaluated based on training,testing,and total time,and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS.展开更多
Up to now, the study on the cardinal number of fuzzy sets has advanced at on pace since it is very hard to give it an appropriate definition. Althrough for it in [1], it is with some harsh terms and is not reasonable ...Up to now, the study on the cardinal number of fuzzy sets has advanced at on pace since it is very hard to give it an appropriate definition. Althrough for it in [1], it is with some harsh terms and is not reasonable as we point out in this paper. In the paper, we give a general definition of fuzzy cardinal numbers. Based on this definition, we not only obtain a large part of results with re spect to cardinal numbers, but also give a few of new properties of fuzzy cardinal numbers.展开更多
The paper introduces the cardinal vowels system invented by the famous English phonetician Daniel Jones.This system enables a teacher to describe to his students a foreign vowel by comparing it with the nearest vowel ...The paper introduces the cardinal vowels system invented by the famous English phonetician Daniel Jones.This system enables a teacher to describe to his students a foreign vowel by comparing it with the nearest vowel in his mother tongue,which makes the learning of a foreign sound much easier to his students.Two cases of teaching Chinese students English vowels are taken as an example to illustrate the point.IPA cardinal vowel system is of use in terms of teaching and learning English.Two suggestions are put forward in the end.展开更多
The response of seed germination to environmental factors can be estimated by nonlinear regression. The present study was performed to compare four nonlinear regression models(segmented, beta, beta modified, and dent-...The response of seed germination to environmental factors can be estimated by nonlinear regression. The present study was performed to compare four nonlinear regression models(segmented, beta, beta modified, and dent-like) to describe the germination rate–temperature relationships of milk thistle(Silybum marianum L.) at six constant temperatures, with the aim of identifying the cardinal temperatures and thermal times required to reach different germination percentiles. Models and statistical indices were calibrated using an iterative optimization method and their performance was compared by root mean square error(RMSE), coefficient of determination(R2) and Akaike information criterion correction(AICc). The beta model was found to be the best model for predicting the required time to reach 50% germination(D50),(R2= 0.99;RMSE = 0.004; AICc =-276.97). Based on the model outputs, the base, optimum, and maximum temperatures of seed germination were 5.19 ± 0.79, 24.01 ± 0.11, and 34.32 ± 0.36 °C,respectively. The thermal times required for 50% and 90% germination were 4.99 and7.38 degree-days, respectively.展开更多
文摘The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale or other inappropriate purposes.Similarly,national and international organizations have country-level and company-level private information that could be accessed by different network attacks.Therefore,the need for a Network Intruder Detection System(NIDS)becomes essential for protecting these networks and organizations.In the evolution of NIDS,Artificial Intelligence(AI)assisted tools and methods have been widely adopted to provide effective solutions.However,the development of NIDS still faces challenges at the dataset and machine learning levels,such as large deviations in numeric features,the presence of numerous irrelevant categorical features resulting in reduced cardinality,and class imbalance in multiclass-level data.To address these challenges and offer a unified solution to NIDS development,this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment.Cardinality reduction was applied to categorical features through the binning method.Subsequently,the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method.Finally,the preprocessed,refined,and oversampled feature set was divided into training and test sets with an 80–20 ratio,and two experiments were conducted.In Experiment 1,the binary classification was executed using four machine learning classifiers,with the extra trees classifier achieving the highest accuracy of 97.23%and an AUC of 0.9961.In Experiment 2,multiclass classification was performed,and the extra trees classifier emerged as the most effective,achieving an accuracy of 81.27%and an AUC of 0.97.The results were evaluated based on training,testing,and total time,and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS.
文摘Up to now, the study on the cardinal number of fuzzy sets has advanced at on pace since it is very hard to give it an appropriate definition. Althrough for it in [1], it is with some harsh terms and is not reasonable as we point out in this paper. In the paper, we give a general definition of fuzzy cardinal numbers. Based on this definition, we not only obtain a large part of results with re spect to cardinal numbers, but also give a few of new properties of fuzzy cardinal numbers.
文摘The paper introduces the cardinal vowels system invented by the famous English phonetician Daniel Jones.This system enables a teacher to describe to his students a foreign vowel by comparing it with the nearest vowel in his mother tongue,which makes the learning of a foreign sound much easier to his students.Two cases of teaching Chinese students English vowels are taken as an example to illustrate the point.IPA cardinal vowel system is of use in terms of teaching and learning English.Two suggestions are put forward in the end.
文摘The response of seed germination to environmental factors can be estimated by nonlinear regression. The present study was performed to compare four nonlinear regression models(segmented, beta, beta modified, and dent-like) to describe the germination rate–temperature relationships of milk thistle(Silybum marianum L.) at six constant temperatures, with the aim of identifying the cardinal temperatures and thermal times required to reach different germination percentiles. Models and statistical indices were calibrated using an iterative optimization method and their performance was compared by root mean square error(RMSE), coefficient of determination(R2) and Akaike information criterion correction(AICc). The beta model was found to be the best model for predicting the required time to reach 50% germination(D50),(R2= 0.99;RMSE = 0.004; AICc =-276.97). Based on the model outputs, the base, optimum, and maximum temperatures of seed germination were 5.19 ± 0.79, 24.01 ± 0.11, and 34.32 ± 0.36 °C,respectively. The thermal times required for 50% and 90% germination were 4.99 and7.38 degree-days, respectively.
