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Addressing the challenge of missing values is a critical step when preparing and analyzing data. This process, known as imputation, helps ensure the dataset is complete, accurate, and reliable. As a result, the possibility of bias and errors in subsequent analysis is significantly reduced. The key contribution of this work is to assess the efficiency of imputation by feature importance employing several base learning algorithms. This study investigates the effectiveness of individual and ensemble machine learning methods as the base learning algorithms, including support vector machines with the linear kernel (SVML), boosted linear regression (BLR), deep boost (DBP), and K-Nearest Neighbor (K-NN), in predicting missingness patterns. The dataset for each category explicitly introduces missingness patterns, including missing not at random (MNAR), at random (MAR), and completely at random (MCAR) at different percentages (15%, 45%, 25%), and Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are among the commonly used performance matrices employed to gauge the effectiveness of the IBFI framework. The dataset for this study, comprising soil radon and thoron gas concentration time series along with meteorological parameters, the dataset spans a 14-month period. Four earthquake events were recorded during the whole study period. The deep boosting model (DBP) consistently outperforms other base learning models in imputing missing values across various variables within the imputation by feature importance (IBFI) framework. Specifically, DBP achieves an average RMSE value of 573.165 for the Radon variable under MCAR scenarios. For the Thoron variable, DBP demonstrates impressive performance with average MAPE values of 0.7405, 0.7249, and 0.8212 under MCAR, MNAR, and MAR conditions respectively. Additionally, DBP yields competitive results for imputing missing entries in Temperature, Relative Humidity, and Pressure variables. These findings highlight effectiveness of DBP in accurately predicting missing values. This study concludes that the IBFI with deep boosting model executes the imputations quite accurately relative to other base learning models. Moreover, this study recommends using DBP as a base learning algorithm in imputation by feature importance framework for uncovering hidden patterns in time series data like soil radon gas. The replication of the study using heterogeneous datasets would enhance the understanding of the generalization and broader applicability of the imputation by feature importance.

This article primarily focuses on the performance evaluation of a new methodology, imputation by feature importance (IBFI), to serve its imputed dataset in further regression scenarios when dealing with soil radon gas concentration (SRGC) time-series data. The time-series data have been collected spanning over fourteen(14) months period, which included four seismic events, and have been used for experimentation. The imputation by feature importance (IBFI) has been experimented and obtained results are found more efficient in the imputation of missing patterns in investigated time series when compared to traditionally used imputation methods viz. mean, median, mode, predictive mean matching (PMM), and hot-deck imputation.The IBFI methodology has been used in a variety of settings, such as data missing not at random (MNAR), missing completely at random (MCAR), and missing at random (MAR), with missingness percentages ranging from 10% to 30%. In this study, the imputed datasets, 9 for each imputation method, have been used further to predict the attribute of interest (radon concentration (RN)) keeping others as independent attributes such as thoron, temperature, relative humidity, and pressure time series. Support vector machine (SVM) with linear kernel has been used as a learning algorithm and its performance was evaluated based on the fact that how efficient and unbiased values were imputed. Statistical performance evaluation measures viz. root mean squared log error (RMSLE), root mean square error (RMSE), mean squared error (MSE),and mean absolute percentage error (MAPE) have been calculated for the assessment of performance. The findings of our study show that the IBFI imputed dataset has provided a better-fitted model. The model generation and predictions upon IBFI imputed time series result in more accurate predictions when compared to mean, median, mode, PMM, and hot-deck imputed time series. Furthermore, PMM and median imputed time series also perform closer to the IBFI imputed time series.

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe to confirm the correlation of radon emission dynamics and earthquakes, and concluded that the soil radon gas is the witness of anomalous behaviour before the occurrences of several earthquakes. This anomalous behavior can help to construct a better prediction model for earthquake forecasting. This paper aims at employing different ensemble and individual machine learning methods on real time radon time series data with different scenarios to predict anomalies in data caused by the seismic activities.The ensemble methods include boosted tree, bagged cart and boosted linear model while standalone machine learning methods include support vector machine with linear and radial kernels and k-nearest neighbors ( ${K}$ -NN). We tested the methods on a dataset recorded on the fault line located in Muzaffarabad. Time series data was collected over a period ranging from March 1, 2017 to May 11, 2018 including nine(09) earthquakes. The methods are tested in four different settings with 10 times 10 folds cross validation procedure over the time window of 1 to 4. The repeated 10 fold cross validation is performed to reduce the noise in the model performance estimation by replicating the 10 fold cross validation procedure 10 times. Statistical performance evaluation measures viz. root mean square error (RMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), percentage bias (PB), and mean squared error (MSE) have been calculated for the assessment of performance. In setting 1, the support vector machine with radial kernel performs better with the minimum RMSE score of 1381.023 when compared to other prediction models. In setting 3, it can be observed through different performance metrics such as RMSE, the value in the range [1262.864, 1409.616] which is minimum when other prediction models for predicting soil radon gas concentration dataset. For setting 4, the boosted tree model yielded the minimum RMSE and MAPE scores of 1573.174 and 0.056 respectively. Findings of the study shows that boosted tree and support vector machine with radial kernel proved to be better regression models for the prediction of anomalies in soil radon gas concentration during seismic activities. An important finding of this study suggests that by employing boosted tree ensemble method make us able to accurately predict soil radon gas concentration automatically from environmental parameters.

In medical field, accurate decisions are very important as they risk human lives. decision support system (DSS) plays important role in making accurate decisions and used for classification/prediction. In gene expression analysis, genes are not only inflated by the external environmental conditions but also the expression values of certain genes are affected (like cancer, obesity etc). in this study, various traditional (Support Vector Machine, Decision Trees, and Linear Discriminant Analysis, naïve Bayes, logistic regression, and multilayer perceptron) and proposed methods (combination of traditional with ensemble and probabilistic classifiers) are used in order to perform the classification and prediction analysis. In this study we used the publicly available datasets comprised of Lymphoid, Leukemia and Colon Cancer. The classification performance on Colon dataset with traditional methods was obtained with accuracy (56%) and proposed probabilistic ensemble methods with accuracy (88%). For dataset, Leukemia, the accuracy was obtained using traditional methods (78%) and proposed methods (92%). Similarly, on Lymphoid dataset, the traditional methods yielded accuracy (75%) and proposed methods (87%). The results revealed that proposed methods yielded the improved detection performance. The proposed methods can be used as a better predictor for early diagnosis and improved diagnosis to improve the healthcare systems.

A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing not at random (MNAR), and missing at random (MAR). IBFI utilizes the feature importance and iteratively imputes missing values using any base learning algorithm. For this work, IBFI is tested on soil radon gas concentration (SRGC) data. XGBoost is used as the learning algorithm and missing data are simulated using R for different missingness scenarios. IBFI is based on the physically meaningful assumption that SRGC depends upon environmental parameters such as temperature and relative humidity. This assumption leads to a model obtained from the complete multivariate series where the controls are available by taking the attribute of interest as a response variable. IBFI is tested against other frequently used imputation methods, namely mean, median, mode, predictive mean matching (PMM), and hot-deck procedures. The performance of the different imputation methods was assessed using root mean squared error (RMSE), mean squared log error (MSLE), mean absolute percentage error (MAPE), percent bias (PB), and mean squared error (MSE) statistics. The imputation process requires more attention when multiple variables are missing in different samples, resulting in challenges to machine learning methods because some controls are missing. IBFI appears to have an advantage in such circumstances. For testing IBFI, Radon Time Series Data (RTS) has been used and data was collected from 1st March 2017 to the 11th of May 2018, including 4 seismic activities that have taken place during the data collection time.

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

A new methodology, the hybrid learning system (HLS), based upon semi-supervised learning is proposed. HLS categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. The technique utilizes the modified breaking ties (MBT) algorithm for active learning and unsupervised learning-based regressors, viz. multinomial logistic regression, for hyperspectral image categorization. The probabilities estimated by multinomial logistic regression for each sample helps towards improved segregation. The high dimensionality leads to a curse of dimensionality, which ultimately deteriorates the performance of remote sensing data classification, and the problem aggravates further if labeled training samples are limited. Many studies have tried to address the problem and have employed different methodologies for remote sensing data classification, such as kernelized methods, because of insensitiveness towards the utilization of large dataset information and active learning (AL) approaches (breaking ties as a representative) to choose only prominent samples for training data. The HLS methodology proposed in the current study is a combination of supervised and unsupervised training with generalized composite kernels generating posterior class probabilities for classification. In order to retrieve the best segmentation labels, we employed Markov random fields, which make use of prior labels from the output of the multinomial logistic regression. The comparison of HLS was carried out with known methodologies, using benchmark hyperspectral imaging (HI) datasets, namely “Indian Pines” and “Pavia University”. Findings of this study show that the HLS yields the overall accuracy of {99.93% and 99.98%}Indian Pines and {99.14% and 99.42%}Pavia University for classification and segmentation, respectively.