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A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

Computer Modeling in Engineering & Sciences 2019年第12期121卷 815-834页

作者： Sasan Saqaeeyan Hamid Haj Seyyed Javadi hossein amirkhani Department of Computer Engineering Borujerd BranchIslamic Azad UniversityBorujerdIran Department of Mathematics and Computer Science Shahed UniversityTehranIran Computer Engineering and Information Technology Department University of QomQomIran

Anomaly detection in smart homes provides support to enhance the health and safety of people who live *** to the previous studies done on this topic,less attention has been given to hybrid *** paper presents a two-steps hybrid probabilistic anomaly detection model in the smart ***,it employs various algorithms with different characteristics to detect anomalies from sensory ***,it aggregates their results using a Bayesian *** this Bayesian network,abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base *** evaluation of a real dataset indicates the effectiveness of the proposed method by reducing false positives and increasing true positives.

关键词： Smart homes sensory data anomaly detection Bayesian networks ensemble method

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Enhancing the Robustness of Visual Object Tracking via Style Transfer

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Computers, Materials & Continua 2022年第1期70卷 981-997页

作者： Abdollah amirkhani Amir hossein Barshooi Amir Ebrahimi School of Automotive Engineering Iran University of Science and TechnologyTehran16846-13114Iran School of Electrical Engineering and Computing The University of NewcastleCallaghan2308Australia

The performance and accuracy of computer vision systems are affected by noise in different *** numerous solutions and algorithms have been presented for dealing with every type of noise,a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still *** this paper,we have focused on the stability and robustness of one computer vision branch(i.e.,visual object tracking).We have demonstrated that,without imposing a heavy computational load on a model or changing its algorithms,the drop in the performance and accuracy of a system when it is exposed to an unseen noise-laden test dataset can be prevented by simply applying the style transfer technique on the train dataset and training the model with a combination of these and the original untrained *** verify our proposed approach,it is applied on a generic object tracker by using regression *** method’s validity is confirmed by testing it on an exclusive benchmark comprising 50 image sequences,with each sequence containing 15 types of noise at five different intensity *** OPE curves obtained show a 40%increase in the robustness of the proposed object tracker against noise,compared to the other trackers considered.

关键词： Style transfer visual object tracking robustness corruption

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