| 1 | Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning显示文摘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. | G.Anitha S.Baghavathi Priya | 2022 | Computer Systems Science & Engineering2022，42，7： | 1 |
| 2 | Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model显示文摘Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%. | G.Anitha N.Supriya Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Jinsang You | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
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