Correlation analysis is an extensively used technique that identifies interesting relationships in data. These relationships help us realize the relevance of attributes with respect to the target class to be predicted. This study has exploited correlation analysis and machine learning-based approaches to identify relevant attributes in the dataset which have a significant impact on classifying a patient’s mental health status. For mental health situations, correlation analysis has been performed in Weka, which involves a dataset of depressive disorder symptoms and situations based on weather conditions, as well as emotion classification based on physiological sensor readings. Pearson’s product moment correlation and other different classification algorithms have been utilized for this analysis. The results show interesting correlations in weather attributes for bipolar patients, as well as in features extracted from physiological data for emotional states.

The aim of facial expression recognition (FER) algorithms is to extract discriminative features of a face. However, discriminative features for FER can only be obtained from the informative regions of a face. Also, each of the facial subregions have different impacts on different facial expressions. Local binary pattern (LBP) based FER techniques extract texture features from all the regions of a face, and subsequently the features are stacked sequentially. This process generates the correlated features among different expressions, and hence affects the accuracy. This research moves toward addressing these issues. The authors' approach entails extracting discriminative features from the informative regions of a face. In this view, they propose an informative region extraction model, which models the importance of facial regions based on the projection of the expressive face images onto the neural face images. However, in practical scenarios, neutral images may not be available, and therefore the authors propose to estimate a common reference image using Procrustes analysis. Subsequently, weighted‐projection‐based LBP feature is derived from the informative regions of the face and their associated weights. This feature extraction method reduces miss‐classification among different classes of expressions. Experimental results on standard datasets show the efficacy of the proposed method.

Balint syndrome is a disorder of inaccurate visually guided saccades, optic ataxia, and simultanagnosia that typically results from bilateral parieto-occipital lesions. Visual perception disturbances in the posterior reversible encephalopathy syndrome (PRES) include hemianopia, visual neglect, and cerebral blindness, but Balint syndrome had not been recognized. We report Balint syndrome associated with PRES in a 37-year-old woman with acute hypertension and systemic lupus erythematosus. Balint syndrome can be an initial presentation of PRES.

In an environment, one of the natural geological hazards is land surface subsidence. There are several reasons for land subsidence among them are underground coal mining and coal fire in subsurface. The deformation is primarily measured in terms of change in ground elevation values (Z-dimension) at different time intervals at identified ground locations. All the conventional and exiting techniques have certain limitations in monitoring and predicting land surface subsidence. In this work, we predict the land subsidence for one year in the interval of twelve days on the datasets collected through a monitoring technique called Modified PSIn-SAR. The sample datasets contains 14 locations and 67 previous land subsidence value calculated from each location. We train and test predictive models and perform the prediction of the land subsidence using Vanilla and Stacked long short-term memories (LSTMs). Finally, we demonstrates the predicted deformation values of the 14 locations for one year.