Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease predict...Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical procedures.Multi-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical diagnostics.DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets.This paper proposed a novel method that deals with the issue of less data dimensionality.Inspired by the regression analysis,the proposed method classifies the data by going through three different stages.In the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications.Extensive experiments were carried out on the Cleveland heart disease dataset.The results show significant improvement in classification accuracy.It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.展开更多
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
Imbalanced datasets are prevalent in real life.The imbalanced datasets pose challenges for classification and regression tasks.Compared to imbalanced classification,imbalanced regression deals with continuous labels.T...Imbalanced datasets are prevalent in real life.The imbalanced datasets pose challenges for classification and regression tasks.Compared to imbalanced classification,imbalanced regression deals with continuous labels.The positional relationship of the labels is unable to reflect the relationship of the feature space.The feature of the rare sample is under-represented due to the influence of frequent samples.The Quantitative Relationship(QuanRel)regularizer is proposed to mitigate the problem of under-representation of features.The effect of frequent samples on the rare samples in the feature space is mitigated by the QuanRel.The QuanRel uses the number of discriminated classes,instead of the information in the label space.The incorrect proximity of features is improved by the QuanRel.Three regression benchmark datasets are used to demonstrate the effectiveness of QuanRel.The results of the models with QuanRel are the best of the common models in the imbalanced regression datasets.展开更多
文摘Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical procedures.Multi-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical diagnostics.DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets.This paper proposed a novel method that deals with the issue of less data dimensionality.Inspired by the regression analysis,the proposed method classifies the data by going through three different stages.In the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications.Extensive experiments were carried out on the Cleveland heart disease dataset.The results show significant improvement in classification accuracy.It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
基金supported by the Research Platforms and Projects of Universities in Guangdong Province,China—Youth Innovative Talents(No.2022KQNCX071).
文摘Imbalanced datasets are prevalent in real life.The imbalanced datasets pose challenges for classification and regression tasks.Compared to imbalanced classification,imbalanced regression deals with continuous labels.The positional relationship of the labels is unable to reflect the relationship of the feature space.The feature of the rare sample is under-represented due to the influence of frequent samples.The Quantitative Relationship(QuanRel)regularizer is proposed to mitigate the problem of under-representation of features.The effect of frequent samples on the rare samples in the feature space is mitigated by the QuanRel.The QuanRel uses the number of discriminated classes,instead of the information in the label space.The incorrect proximity of features is improved by the QuanRel.Three regression benchmark datasets are used to demonstrate the effectiveness of QuanRel.The results of the models with QuanRel are the best of the common models in the imbalanced regression datasets.
