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.

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 onsoil 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 from1^stMarch 2017to the11^thof May 2018, including4seismic activities that have taken place during the data collection time.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations’ sustainability development goals.

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.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis.