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Sökning: WFRF:(Fotouhi A)

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  • Abbaspour, Saadeh, et al. (författare)
  • A comparative analysis of hybrid deep learning models for human activity recognition
  • 2020
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:19
  • Tidskriftsartikel (refereegranskat)abstract
    • Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. © 2020 by the authors.
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  • Deldjoo, Yashar, 1984, et al. (författare)
  • A Novel Fuzzy-Based Smoke Detection System Using Dynamic and Static Smoke Features
  • 2015
  • Ingår i: 2015 23rd Iranian Conference on Electrical Engineering. - 2164-7054. - 9781479919710 ; 10, s. 729-733
  • Konferensbidrag (refereegranskat)abstract
    • Automatic fire surveillance is an important task for providing emergency response in the event of unexpected fire hazards. Early detection of fire can substantially mitigate the ecological or economical costs associated with a fire disaster. In this regard, as smoke usually always precedes fire, an intelligent smoke detection system is proposed that exploits a Fuzzy Inference System (FIS) in order to aggregate the features of smoke. In addition, robust smoke feature detection algorithms are implemented that take into account both dynamic and static characteristics of smoke. The smoke features include motion, motion orientation (estimated by using the accumulation of motion) for the former and texture for the latter. Experimental results on different video frames show that the proposed smoke detection system has robust performance on detecting the existence of smoke which shows the effectiveness of the proposed smoke detection system.
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  • Resultat 1-19 av 19

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