Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak

Health informatics is an interdisciplinary field in the computer and health sciences. Health informatics, which enables the effective use of medical information, has the potential to reduce both the cost and the burden of healthcare workers during the pandemic process. Using the machine learning algorithms support vector machines, naive bayes, k-nearest neighbor, and C4.5 algorithms, a model performance evaluation was performed to identify the algorithm that will show the highest performance for the prediction of the disease. Three separate training and test datasets were created 70% - 30%, 75% - 25%, and 80% - 20%, respectively. The implementation phase of the study was carried out by following the CRISP-DM steps, and the analyses were made using the R language. By examining the model performance evaluation criteria, the findings show that the C4.5 algorithm showed the best performance with 70% training dataset.

Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction

Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models’ ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.

On the evaluation of both spatial and temporal performance of distributed hydrological models

Evaluating the spatial and temporal model performance of distributed hydrological models is necessary to ensure that the simulated spatial and temporal patterns are meaningful. In recent years, spatial and temporal remote sensing data have been increasingly used for model performance evaluation. Previous studies, however, have focused on either the temporal or spatial model performance evaluation. In addition, temporal (or spatial) model performance evaluation is often done in a spatially (or temporally) lumped approach. Here, we evaluated (1) the temporal model performance evaluation in a spatially distributed approach (spatiotemporal) and (2) the spatial model performance in a temporally distributed approach (temporospatial) model performance evaluation. This study demonstrated that both spatiotemporal and temporospatial model performance evaluations are necessary since they provide different aspects of the model performance. For example, spatiotemporal model performance evaluation helps in detecting the areas with an issue in the simulated temporal patterns. However, temporospatial model performance evaluation helps in detecting the time with an issue in the simulated spatial patterns. The results also show that an increase in the spatiotemporal model performance will not necessarily lead to an increase in the temporospatial model performance and vice versa, depending on the evaluation statistics. Overall, this study has highlighted the necessity of a joint spatiotemporal and temporospatial model performance evaluation to understand/improve spatial and temporal model behavior/performance.

Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

The main aim of this study is the assessment and discussion of a model for hand-written Arabic through segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting images to binary type. In the segmentation step, first removed the small diacritics then bounded a connected component to segment offline words. Huge data was utilized in the proposed model for applying a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then segmented into sub-words. After small gaps been connected, the model performance evaluation had been reached 88% against the standard ground truth of the database. The proposed model achieved the highest accuracy when compared with the related works.

Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction

Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic, and are affected by a number of shortcomings (e.g., failure to assess models' ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields like machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility; identify concrete steps the community could take to promote the development of such benchmarks; and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology, and encourage researchers to place heavier emphasis on the practical utility of scientific models.