CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin...CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.展开更多
Application Programming Interface(API)call feature analysis is the prominent method for dynamic android malware detection.Standard benchmark androidmalware API dataset includes featureswith high dimensionality.Not all...Application Programming Interface(API)call feature analysis is the prominent method for dynamic android malware detection.Standard benchmark androidmalware API dataset includes featureswith high dimensionality.Not all features of the data are relevant,filtering unwanted features improves efficiency.This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance.In the first phase fuzzy benchmarking is used to select the top best features,and in the second phase meta-heuristic optimization algorithms viz.,Moth Flame Optimization(MFO),Multi-Verse Optimization(MVO)&Whale Optimization(WO)are run with Machine Learning(ML)wrappers to select the best from the rest.Five ML methods viz.,Decision Tree(DT),Random Forest(RF),K-NearestNeighbors(KNN),Naie Bayes(NB)&NearestCentroid(NC)are compared as wrappers.Several experiments are conducted and among them,the best post reduction accuracy of 98.34% is recorded with 95% elimination of features.The proposed novelmethod outperformed among the existing works on the same dataset.展开更多
Traditional Chinese medicine （TCM） relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of...Traditional Chinese medicine （TCM） relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of these combined effects can become computationally intractable. Thus feature selection has become increasingly crucial as a pre-processing step prior to the study of combined effects in TCM informatics. In accord with this goal, a new feature se- lection algorithm known as a co-evolving memetic wrapper （COW） is proposed in this paper. COW takes advantage of recent research in genetic algorithms （GAs） and memetic al- gorithms （MAs） by evolving appropriate feature subsets for a given domain. Our empirical experiments have demonstrated that COW is capable of selecting subsets of herbs from a TCM insomnia dataset that shows signs of combined effects on the prediction of patient outcomes measured in terms of classification accuracy. We compare the proposed algorithm with results from statistical analysis including main effects and up to three way interaction terms and show that COW is capable of correctly identifying the herbs and herb by herb effects that are significantly associated to patient outcome prediction.展开更多
文摘CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
文摘Application Programming Interface(API)call feature analysis is the prominent method for dynamic android malware detection.Standard benchmark androidmalware API dataset includes featureswith high dimensionality.Not all features of the data are relevant,filtering unwanted features improves efficiency.This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance.In the first phase fuzzy benchmarking is used to select the top best features,and in the second phase meta-heuristic optimization algorithms viz.,Moth Flame Optimization(MFO),Multi-Verse Optimization(MVO)&Whale Optimization(WO)are run with Machine Learning(ML)wrappers to select the best from the rest.Five ML methods viz.,Decision Tree(DT),Random Forest(RF),K-NearestNeighbors(KNN),Naie Bayes(NB)&NearestCentroid(NC)are compared as wrappers.Several experiments are conducted and among them,the best post reduction accuracy of 98.34% is recorded with 95% elimination of features.The proposed novelmethod outperformed among the existing works on the same dataset.
文摘Traditional Chinese medicine （TCM） relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of these combined effects can become computationally intractable. Thus feature selection has become increasingly crucial as a pre-processing step prior to the study of combined effects in TCM informatics. In accord with this goal, a new feature se- lection algorithm known as a co-evolving memetic wrapper （COW） is proposed in this paper. COW takes advantage of recent research in genetic algorithms （GAs） and memetic al- gorithms （MAs） by evolving appropriate feature subsets for a given domain. Our empirical experiments have demonstrated that COW is capable of selecting subsets of herbs from a TCM insomnia dataset that shows signs of combined effects on the prediction of patient outcomes measured in terms of classification accuracy. We compare the proposed algorithm with results from statistical analysis including main effects and up to three way interaction terms and show that COW is capable of correctly identifying the herbs and herb by herb effects that are significantly associated to patient outcome prediction.
