ediction Model for a Good Learning Environment Using an Ensemble Approach

This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)classifier.It consists of a series of modules;data preprocessing,data normalization,data split andfinally classification or prediction by the RF classifier.The preprocessed data is normalized using minmax normalization often used before modelfitting.As the input data or variables are measured at different scales,it is necessary to normalize them to contribute equally to the modelfitting.Then,the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation(k=10)is used to validate the model.The proposed Prediction Model for Course Selection(PMCS)system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels,namely low,medium and high.It is operated under two modes;locally and globally.The former considers the gender of the learner and the later does not consider the gender of the learner.The database comprises the learner opinions from 75 males and 75 females per category(low,medium and high).Thus the system uses a total of 450 samples to evaluate the performance of the PMCS system.Results show that the system’s performance,while using locally i.e.,gender-wise has slightly higher performance than the global system.The RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%,whereas in the local system it is 97%(male)and 97.6%(female).The overall performance of the RF classifier with 75 trees is better than 25,50 and 100 decision trees in both local and global systems.

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

Human fall detection plays a vital part in the design of sensor based alarming system,aid physical therapists not only to lessen after fall effect and also to save human life.Accurate and timely identification can offer quick medical ser-vices to the injured people and prevent from serious consequences.Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments.At present times,deep learning(DL)models par-ticularly convolutional neural networks(CNNs)have gained much importance in the fall detection tasks.With this motivation,this paper presents a new vision based elderly fall event detection using deep learning(VEFED-DL)model.The proposed VEFED-DL model involves different stages of operations namely pre-processing,feature extraction,classification,and parameter optimization.Primar-ily,the digital video camera is used to capture the RGB color images and the video is extracted into a set of frames.For improving the image quality and elim-inate noise,the frames are processed in three levels namely resizing,augmenta-tion,and min–max based normalization.Besides,MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed frames.In addition,the extracted spatial features are then fed into the gated recur-rent unit(GRU)to extract the temporal dependencies of the human movements.Finally,a group teaching optimization algorithm(GTOA)with stacked autoenco-der(SAE)is used as a binary classification model to determine the existence of fall or non-fall events.The GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be enhanced.In order to assess the fall detection performance of the presented VEFED-DL model,a set of simulations take place on the UR fall detection dataset and multi-ple cameras fall dataset.The experimental outcomes highlighted the superior per-formance of the presented method over the recent methods.