Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control...Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.展开更多
In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making d...In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.展开更多
Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important paramete...Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important parameter for describing the protein corona.For the first time,we comprehensively predicted the RPA of multiple proteins on the protein corona.First,we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle,which is dichotomous prediction.Then,we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA,which is regression prediction.Meanwhile,we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis.Finally,we mined important features about the RPA prediction,which provided effective suggestions for the preliminary design of protein corona.The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.展开更多
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493).
文摘Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.
文摘In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.
基金supported by the National Natural Science Foundation of China(nos.62i01100 and 62262015).
文摘Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important parameter for describing the protein corona.For the first time,we comprehensively predicted the RPA of multiple proteins on the protein corona.First,we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle,which is dichotomous prediction.Then,we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA,which is regression prediction.Meanwhile,we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis.Finally,we mined important features about the RPA prediction,which provided effective suggestions for the preliminary design of protein corona.The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.
