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

Department of Information Security

Point-of-interest (POI) recommendation has become a favorite topic on location-based cyber-physical–social networks (LBCPSNs). The geographical influence of locations (e.g., geographical proximity) and the social influence of friends (e.g., social ties) have been widely used in many existing POI recommendation methods, most of which paid little attention to the sequential influence and temporal influence of locations on users’ check-in behaviors. However, human mobility exhibits sequential and temporal patterns. In this paper, we propose a unified probabilistic generative model, i.e., a multi-modal Bayesian embedding model (MMBE), to discover the social, sequential, temporal, and spatial patterns of users’ check-in behaviors simultaneously. Besides, MMBE can model the joint effect of the four factors mentioned above on the decision-making for POI selection across heterogeneous workflow activities in cyber-physical–social(CPS) systems. Then, we conduct an in-depth performance evaluation for MMBE using two large-scale real-world datasets from Gowalla and Brightkite. Experimental results show that our proposed method outperforms the other five state-of-the-art POI recommendation approaches regarding Precision and Recall. © 2017 Elsevier B.V.

World University root node

School of Computer Science and Engineering

Vellore Campus

Future Generation Computer Systems

Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical–social networks

With the development of technology and computer systems, web services are used to develop business processes. Since a web service only performs a simple operation, web services composition has become important to respond to these business processes. In recent times, the number of existing web services has grown increasingly; therefore, similar services are presented increasingly. These similar web services are discriminated based on the various quality of service (QoS) parameters. These quality parameters include cost, execution time, availability, and reliability. In order to have the best QoS, each user should select a subset of services that presents best quality parameters. On the other hand, due to huge number of services, selecting web services for composition is an NP-hard optimization problem. This paper presents an efficient method for solving this problem using biogeography-based optimization (BBO). BBO is a very simple algorithm with few control parameters and effective exploit. The proposed method offers promising solutions to this problem. Evaluation and simulation results indicate efficiency and feasibility of the proposed algorithm. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Soft Computing

A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm

The rapid growth of the Internet promotes the growth of textual data, and people get the information they need from the amount of textual data to solve problems. The textual data may include some potential information like the opinions of the crowd, the opinions of the product, or some market-relevant information. However, some problems that point to “How to get features from the text” must be solved. The model of extracting the text features by using the neural network method is called neural network language model. The features are based on n-gram Model concept, which are the co-occurrence relationship between the vocabularies. The word vectors are important because the sentence vectors or the document vectors still have to understand the relationship between the words, and based on this, this study discusses the word vectors. This study assumes that the words contain “the meaning in sentences” and “the position of grammar.” This study uses recurrent neural network with attention mechanism to establish a language model. This study uses Penn Treebank, WikiText-2, and NLPCC2017 text datasets. According to these datasets, the proposed models provide the better performance by the perplexity. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

Neural Computing and Applications

Recurrent neural network with attention mechanism for language model

Robust and accurate visual tracking is a challenging problem in computer vision. In this paper, we exploit spatial and semantic convolutional features extracted from convolutional neural networks in continuous object tracking. The spatial features retain higher resolution for precise localization and semantic features capture more semantic information and less fine-grained spatial details. Therefore, we localize the target by fusing these different features, which improves the tracking accuracy. Besides, we construct the multi-scale pyramid correlation filter of the target and extract its spatial features. This filter determines the scale level effectively and tackles target scale estimation. Finally, we further present a novel model updating strategy, and exploit peak sidelobe ratio (PSR) and skewness to measure the comprehensive fluctuation of response map for efficient tracking performance. Each contribution above is validated on 50 image sequences of tracking benchmark OTB-2013. The experimental comparison shows that our algorithm performs favorably against 12 state-of-the-art trackers. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Multimedia Tools and Applications

Spatial and semantic convolutional features for robust visual object tracking

Human wearable helmet is a useful tool for monitoring the status of miners in the mining industry. However, there is little research regarding human emotion recognition in an extreme environment. To the best of our knowledge, this paper is the first to describe the human anxiety change rule and to propose a cloud computing platform for detecting human emotions using brain-computer interface (BCI) devices. In this paper, an emotional state evoked paradigm is designed to identify the brain area where the emotion feature is most evident. Next, the correct electrode position is determined for the collection of the negative emotion by the electroencephalograph (EEG) based on the international 10–20 system of electrode placement. Next, a fusion algorithm of the anxiety level is proposed to evaluate the person’s mental state using the θ, α, and β rhythms of an EEG. Next, the human smart helmet system is designed to collect the human state, which includes the mental parameters of the anxiety level, the fatigue level, the concentration level, and the environmental parameters in the coal mine. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most visible EEG changes appear within the first 2 s following stimulation. The amplitudes of the θ rhythm increase most significantly in the negative emotional state. The fusion algorithm of the anxiety level accurately measures negative emotional change. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Mobile Networks and Applications

Human Emotion Recognition Using an EEG Cloud Computing Platform

Stimulated by recent experimental and theoretical interest in coronoids, a class of polycyclic aromatic compounds, we have developed graph-theoretically based topological indices for the characterization of a number of coronoid compounds. Coronoids have been the focus of several studies due to superaromaticity and other electronic and geometric properties exhibited by these polycyclics that make them also suitable for many applications due to the presence of cavities of different sizes. We have computed various topological indices for coronoid polycyclic aromatic hydrocarbons with cavities that vary topologically. Exact analytical expressions of a series of coronoid compounds have been computed by means of strength-weighted graphs based on the transitive closure of Djoković-Winkler relation. © 2018, © 2018 Taylor &amp; Francis Group, LLC.

VIT Unitversity

Mathematics

School of Advanced Sciences

Chennai Campus

Department of Mathematics

Polycyclic Aromatic Compounds

Topological Characterization of Coronoid Polycyclic Aromatic Hydrocarbons

Chaotic maps play an important role in information sharing. In this paper a grey wolf optimiser used with reduced entropy-based 3D chaotic map. The selection and high coefficients are selected based on the reduced entropy value to identify the optimised parameters to get unpredictable random values. Time complexity, autocorrelation of V, H and D elements, histogram of original and cipher images, peak signal to noise ratio and NPCR and UACI values are computed from the cipher image. The empirical results show the proposed method provides good, better imperceptibility and defends various attacks. To prove this accomplishment of the method, several experiments were conducted and compared the results with existing systems.

International Journal of Computer Aided Engineering and Technology

<figure class="table"><table><tbody><tr><td> </td><td> </td></tr><tr><td> </td><td><figure class="image"><img src="file:///C:\Users\Admin\AppData\Local\Temp\msohtmlclip1\01\clip_image001.gif"/></figure></td></tr></tbody></table></figure>  <figure class="image"><img src="file:///C:\Users\Admin\AppData\Local\Temp\msohtmlclip1\01\clip_image002.gif"/></figure>An efficient image system-based grey wolf optimiser method for multimedia image security using reduced entropy-based 3D chaotic map

Despite the development of new technologies in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. In order to avoid auto-theft attacks, in this paper we propose a machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors. We consider a dataset composed by 51 different features extracted by 10 different drivers, evaluating the efficiency of the proposed method in driver identification. We also find the most relevant features able to discriminate the car owner by an impostor. We obtain a precision and a recall equal to 99% evaluating a dataset containing data extracted from real vehicle. © 2017 Elsevier Ltd

Computers & Electrical Engineering

Human behavior characterization for driving style recognition in vehicle system

It is generally accepted that the edge computing paradigm is regarded as capable of satisfying the resource requirements for the emerging mobile applications such as the Internet of Things (IoT) ones. Undoubtedly, the data collected by underlying sensor networks are the foundation of both the IoT systems and IoT applications. However, due to the weakness and vulnerability to attacks of underlying sensor networks, the data collected are usually untrustworthy, which may cause disastrous consequences. In this article, a new model is proposed to collect trustworthy data on the basis of edge computing in the IoT. In this model, the sensor nodes are evaluated from multiple dimensions to obtain accurately quantified trust values. Besides, by mapping the trust value of a node onto a force for the mobile data collector, the best mobility path is generated with high trust. Moreover, a mobile edge data collector is used to visit both the sensors with quantified trust values and collect trustworthy data. The extensive experiment validates that the IoT systems based on trustworthy data collection model gain a significant improvement in their performance, in terms of both system security and energy conservation. © 2014 IEEE.

IEEE Internet of Things Journal

Edge-Computing-Based Trustworthy Data Collection Model in the Internet of Things

Therapeutic issues are commonly found in every single person. Tumor is a standout among the most unsafe sicknesses a human can ever had. It is exceptionally hard to distinguish it in its beginning times as its side effects seem just in the progressed stages. Subsequently, the early forecast of lung growth is compulsory for the analysis procedure, and it gives the higher possibilities for fruitful treatment. It is the most difficult approach to upgrade a patient’s possibility for survival. Henceforth, a higher-order neural network system called recurrent neural network with Levenberg–Marquardt model with the help of glowworm swarm optimization algorithm is proposed for managing multimodal disease information. The execution of the proposed strategies is tried with information and the benchmark dataset, and the outcomes demonstrate that the higher-order recurrent neural systems with glowworm swarm optimization give better accuracy of 98% in comparison with customary optimized neural network. © 2018, The Natural Computing Applications Forum.