Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes over...Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.展开更多
Recycling of industrial waste is one of the effective ways to overcome their disposal problem. Ash produced by thermal power plants and lime sludge produced by paper mills require huge disposal land and may create env...Recycling of industrial waste is one of the effective ways to overcome their disposal problem. Ash produced by thermal power plants and lime sludge produced by paper mills require huge disposal land and may create environmental problems such as dusting and leaching of harmful heavy metals. Stabilization of the ash can improve its engineering properties and address the environmental problems. This paper reports the laboratory test results of a Class F pond ash stabilized with lime(2%, 4%, 6% and 8% by weight)alone and in combination with lime sludge(5%, 10% and 15% by weight). The X-ray diffraction(XRD) and scanning electron micrograph(SEM) tests were also performed to identify the possible formation of crystalline phases after stabilization. The effects of lime sludge on the unsoaked and soaked bearing ratios of pond ash with different lime contents, after 7 d, 28 d and 45 d of curing, were observed. Test results indicated that the bearing ratio increased considerably up to a 4% lime content which can be taken as the optimum lime content. Further increase in lime content increased bearing ratio gradually but at a slower rate. The effect of lime sludge was more pronounced at the optimum lime content,particularly at a low curing period. Lime sludge improved the bearing ratio in soaked condition significantly. Leachate analysis of stabilized ash was performed using toxicity characteristic leaching procedure(TCLP-1311) method. The concentrations of toxic elements Zn, Cu, Cd, Ni and Cr in the stabilized mixes were lower than those in the unstabilized waste. The results indicated that the pond ash-lime-lime sludge mixes have potential application as road subbase material.展开更多
基金The authors would like to extend their gratitude to Universiti Teknologi PETRONAS (Malaysia)for funding this research through grant number (015LA0-037).
文摘Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.
文摘Recycling of industrial waste is one of the effective ways to overcome their disposal problem. Ash produced by thermal power plants and lime sludge produced by paper mills require huge disposal land and may create environmental problems such as dusting and leaching of harmful heavy metals. Stabilization of the ash can improve its engineering properties and address the environmental problems. This paper reports the laboratory test results of a Class F pond ash stabilized with lime(2%, 4%, 6% and 8% by weight)alone and in combination with lime sludge(5%, 10% and 15% by weight). The X-ray diffraction(XRD) and scanning electron micrograph(SEM) tests were also performed to identify the possible formation of crystalline phases after stabilization. The effects of lime sludge on the unsoaked and soaked bearing ratios of pond ash with different lime contents, after 7 d, 28 d and 45 d of curing, were observed. Test results indicated that the bearing ratio increased considerably up to a 4% lime content which can be taken as the optimum lime content. Further increase in lime content increased bearing ratio gradually but at a slower rate. The effect of lime sludge was more pronounced at the optimum lime content,particularly at a low curing period. Lime sludge improved the bearing ratio in soaked condition significantly. Leachate analysis of stabilized ash was performed using toxicity characteristic leaching procedure(TCLP-1311) method. The concentrations of toxic elements Zn, Cu, Cd, Ni and Cr in the stabilized mixes were lower than those in the unstabilized waste. The results indicated that the pond ash-lime-lime sludge mixes have potential application as road subbase material.
