The healthcare systems are extensively being used with increased focus on safety of patients. Software engineering for healthcare applications is an emerging research area. Detecting defects is a critical step of software development process of healthcare applications. The performance of the Software Defect Prediction model (SDP) depends on the features of healthcare system; irrelevant features decrease the performance of the model. An optimized feature selection technique is needed to recognize and remove the irrelevant features. In this study, a new optimized feature selection technique, i.e., multiobjective Harris Hawk Optimization (HHO), is proposed for binary classification problem with Adaptive Synthetic Sampling (ADASYN) Technique. Multiobjective HHO is proposed with two main objectives, one to reduce the total amount of selected features and the other to maximize the performance of the proposed model. The multiobjective feature selection technique helps to find the optimal solution to achieve the desired objectives and increase the classification performance in terms of accuracy, AUC, precision, recall, and F1-measure. The study conducts an experiment on a healthcare dataset. Six different search techniques (RF, SVM, bagging, adaptive boosting, voting, and stacking) are implemented on the dataset. The proposed model helps to predict the software defects with a significant classification accuracy of 0.990 and AUC score of 0.992.
Describing the processes leading to deforestation is essential for the development and implementation of the forest policies. In this work, two different learning models were developed in order to identify the best possible model for the assessment of the deforestation causes and trends. We developed autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) independently in order to see the trend between tree cover loss and carbon dioxide emission. This study includes the twenty-year data of Pakistan on tree cover loss and carbon emission from the Global Forest Watch (GFW) platform, a known platform to get numerical data. Minimum mean absolute error (MAE) for the prediction of tree cover loss and carbon emission obtained through ARIMA model is 0.89 and 0.95, respectively. The minimum MAE given by LSTM model is 0.33 and 0.43, respectively. There is no such kind of study conducted in order to identify the increase in carbon emission due to tree cover loss most specifically in Pakistan. The results endorsed that one of the main causes of increase in the pollution in the environment in terms of carbon emission is due to tree cover loss.
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