With rapid development of computer vision and artificial intelligence, cities are becoming more and more intelligent. Recently, since intelligent surveillance was applied in all kind of smart city services, object tracking attracted more attention. However, two serious problems blocked the development of visual tracking in real applications. The first problem is its lower performance under intense illumination variation, while the second issue is its slow speed. This paper addressed these two problems by proposing a correlation filter-based tracker. Fog computing platform was deployed to accelerate the proposed tracking approach. The tracker was constructed by multiple positions' detections and alternate templates. The detection position was repositioned according to the estimated speed of target by an optical flow method, and the alternate template was stored with a template update mechanism, which is all computed at the edge. Experimental results on large-scale public benchmark data sets showed the effectiveness of the proposed method in comparison with state-of-the-art methods. © 2013 IEEE.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Liu G

Liu S

Muhammad K

Doctor F.

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Object Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces

IEEE Access

In recommender systems (RS), many models are designed to predict ratings of items for the target user. To improve the performance for rating prediction, some studies have introduced tags into recommender systems. Tags benefit RS considerably, however, they are also redundant and ambiguous. In this paper, we propose a hybrid deep learning model TRSDL (tag-aware recommender system based on deep learning) to improve the performance of tag-aware recommender systems (TRS). First, TRSDL uses pre-trained word embeddings to represent user-defined tags, and constructs item and user profiles based on the items' tags set and users' tagging behaviors. Then, it utilizes deep neural networks (DNNs) and recurrent neural networks (RNNs) to extract the latent features of items and users, respectively. Finally, it predicts ratings from these latent features. The model not only addresses tag limitations and takes advantage of semantic tag information but also learns more advanced implicit features via deep structures. We evaluated our proposed approach and several baselines on MovieLens-20 m, and the experimental results demonstrate that TRSDL significantly outperforms all the baselines (including the state-of-the-art models BiasedMF and I-AutoRec). In addition, we also explore the impacts of network depth and type on model performance. © 2018 by the authors.

Applied Sciences

TRSDL: Tag-Aware Recommender System Based on Deep Learning–Intelligent Computing Systems

Many applications, such as intelligent transportation systems and mobile multimedia, use Internet of Vehicles (IoV). In IoV, users often access multimedia content from anywhere using Internet connectivity to remote video streaming servers. Due to the high mobility of the nodes in IoV, however, maintaining quality of service (QoS) for these video streaming applications with respect to parameters such as jitter, throughput, buffering, and transmission delays is a challenging task. Especially in the urban environment, the performance of a video streaming protocol is significantly affected by the variation of the traffic density. As a result, how to effectively analyze the impact of traffic density as well as vehicular mobility on the QoS of video streaming is the key for the routing protocol design in IoV. In this paper, based on the relationship between the traffic density and the latency characteristics in urban environment, two models are proposed to accurately estimate the video streaming latency according to the average inter-vehicle distance and radio range. After that, an optimal routing strategy is given by selecting the path from all available paths to minimize the experienced latency for video streaming. Numerical results show that in urban environments, our proposed model has high accuracy under different configurations. © 2017 Elsevier B.V.

Computer Communications

Latency estimation based on traffic density for video streaming in the internet of vehicles

Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85–89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic. © 2016 Elsevier Ltd

Expert Systems with Applications

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

Texture descriptors for representing feature vectors

IEEE Transactions on Industrial Informatics

2018 Index IEEE Transactions on Industrial Informatics Vol. 14

Future Generation Computer Systems

Image steganography using uncorrelated color space and its application for security of visual contents in online social networks

Journal	Data powered by TypesetIEEE Access
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers (IEEE)
ISSN	21693536
Open Access	Yes