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 Database Systems

In present days, mental pressure is getting to be real medical problems. It distinguishes to recognize pressure opportune in proactive way. By extending the predominance digital life, person using to sharing their step-by-step activities and speaking to companions by means of electronic systems administration locales in various ways, so it makes viable to utilize online electronic life data for stress acknowledgment. Here, we discover clients' stress state which is immovably related to that of companions in their Web/public activity, and also, we use different sizes of dataset in various states which is identified with public activity to productively look at the relationship of clients' stress states and connecting within public activity. First, we identify the clients' stress states like visual pictures, writings and group properties with alternate points of view and further move on novel model to show. Dynamic factor graph model (DFGM) is combined with convolutional neural network (CNN) to utilize tweets and group cooperation information for stress disclosure and furthermore which can identify some dependencies locally and scale of invariance in discourse of speech recognitions and also in image recognition (i.e., smileys). The most part of FGM focuses on mood cast technique which is dependent on unique persistent in demonstrating and predicts the user’s emotions in social life. By further examining the social life of the user’s data, we can likewise find many scenarios and the amount of social models in form of scattered connection of stress related to users and also with non-stressed users. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

School of Computer Science and Engineering

Vellore Campus

Department of Information Security

Department of Software Systems

Lecture Notes in Electrical Engineering

Hybrid Model for Stress Detection in Social Media by Using Dynamic Factor Graph Model and Convolutional Neural Networks

The poor security and larger number of IoT devices are highly possible to be snatched and results in Distributed Denial of Service attack. The IoT attacks corrupt the availability of particular nodes or the whole network by jamming the signal or exhausting the resource nodes’ battery. The devices of IoT are facing security problems because of the increase in attacks which are launched by violators during data transmission via internet. To detect such attacks, a hybrid optimization approach usinga hybrid Metaheuristic lion optimization algorithm and Firefly optimization algorithm (ML-F) is proposed. The NSL-KDD and NBaIoT datasets are used where the input data is pre-processed to remove the noises and the missing data. After preprocessing the data, feature extraction is undergone using Recursive feature elimination (RFE).The low rate attacks are selected after splitting the data using a hybrid ML-F optimization algorithm. After selecting the features,a random forest classifier is used for the process of classifying the attacks. The proposed hybrid ML-F method achieves higher performance than the existing gradient boost classifier method,in classifying the attacks. The proposed hybrid ML-F method achieved an accuracy of 99.98%, precision of 99.87%, recall of 100% and f-measure of 99.73%. The existing gradient boosting classifier method shows the Accuracy of 50.93%, Precision of 54.87%, Recall of 77.67% and F-measure of 64.34%. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.

World University root node

International Journal of Systems Assurance Engineering and Management

Attack detection in IoT devices using hybrid metaheuristic lion optimization algorithm and firefly optimization algorithm

The long-term corrosion behavior of IN-RAFM steel in molten Pb-Li upto 10, 000 h of exposure was studied in a rotating disc corrosion test facility at 823 K. As-received IN-RAFMS material possessed an air-formed surface layer containing oxides of Fe, Ce and W. Precipitation of chromium carbides at the grain/lath boundaries of as-received IN-RAFMS led to grain boundary attack by liquid Pb-Li during the initial “Incubation Period” which depicted slow dissolution of surface oxides and lesser corrosion rate. Complete dissolution of oxide layer altered the corrosion mechanism from grain boundary attack to matrix dissolution thereby increasing the corrosion rate. © 2021 Elsevier Ltd

Corrosion Science

Effect of oxide layer and the duration of exposure on the liquid metal corrosion mechanism of RAFM steel in molten Pb-Li

In this paper, we predict the deformation behavior of alloy 690 by explicitly modeling finite thickness grain boundaries within the crystal plasticity framework. This is realized by using a blend of quasi-static tensile experiments, microstructural characterization using scanning electron microscopy, atomistic simulations and crystal plasticity finite element analysis. We begin with material characterization to probe the type of grain boundaries present in the Ni-based alloy 690. Inspired by physically justified multiscale modeling schemes, we then use atomistic simulations within the framework of embedded-atom method to quantify activation parameters for dislocation nucleation from those grain boundaries in Ni bicrystals. The kinetic activation parameters are then passed on to a transition state theory based crystal plasticity model. At this scale, the grain boundaries are explicitly modeled using finite elements and prescribed distinct constitutive parameters obtained from lower scale atomistic simulations. The predictive capabilities of the adopted methodology are demonstrated by capturing the uniaxial tensile behavior of alloy 690 at different temperatures (250C–6000C) (stress-strain curves as well as texture evolution). Our implementation of grain boundaries in crystal plasticity is built upon a standard representative volume element used in most polycrystalline simulations. It is envisioned that this comprehensible approach will provide a compelling platform for unifying many other interface related deformation processes at the mesoscale. Eventually, the demonstrated methodology can expedite in apprehending the structure-property relationships through apt use of physically justified multiscale modeling schemes. © 2021 Acta Materialia Inc.

Materialia

Atomistically informed crystal plasticity analysis of deformation behavior of alloy 690 including grain boundary effects

The proposition of a new algorithm facilitates the predictability of weak/strong monsoons that lead to drought/flood events, respectively, in the Indian summer monsoon rainfall (ISMR). The proposed method estimates skewed Gaussian kernel distribution in the extreme values extracted from the rainfall series and confidence levels of drought and flood years are obtained using bootstrap. Using the selected Coupled Model Intercomparison Phase 5 (CMIP5) simulations under representative concentration pathways (RCP) 8.5 scenario, the proposed method detects that extreme droughts (at 99{\%} confidence level) in India are likely to occur in 2024 and 2027 in the early 21st century. Similarly, models project that 2031, 2032 and 2033 will be the most prominent flood years. It is projected that the probability of drought occurrence is likely to increase by 16{\%}. In contrast, it is expected to diminish flood events by 11{\%} in the future under projected global warming. Notably, our analysis reveals that 23.4{\%} of grids covering {\~{}}30{\%} of the Indian region are likely to experience increased frequency and intensity of droughts during 2020–2029, mainly covering the Northeast, Central and Southern India. Furthermore, during this period, the Northeast and some parts in the North would experience floods over 29.6{\%} (which covers {\~{}} 39{\%}) of the total grids. The proposed algorithm may be used for drought and flood monitoring over any geographical terrain.

Theoretical and Applied Climatology

A novel method to detect drought and flood years in Indian rainfall associated with weak and strong monsoon

This paper focuses to solve two important problems faced in this digital era. The first problem is handling large amount of digital data. Digital sectors are finding difficulty in handling repetitive works and standardization of process. Our University is a eco-friendly, digital university each and every work of our students are maintained digitally. So for a course instructor to open, view, correct and finding plagiarism in the assignment or homework submitted by students is a tough task. So in this paper this tough task is simplified by applying Robotic Process Automation (RPA). The process of downloading and reading the students’ digital document is automated. The second problem is the plagiarism. There has been a massive threat in the academic and technological integrity of innovation in the name of paraphrasing. In a survey conducted by Donald McCabe of Rutgers University over 70 percent of students admit to paraphrasing and copying of others work. They have also fabricated bibliography. This problem can be addressed using the state-of-the-art document similarity deep learning neural networks. In this paper, we propose the design and implementation of a Siamese Neural Network (SNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to identify paraphrasing, using only features of similarity between words where the dataset is evolutionary and the model keeps learning with incremental sources of the document’s plagiarism. This paper will also provide the Tensorflow based implementation of such a model. The proposed system will predict the similarity between documents and predict the plagiarism percentage.

Department of Analytics

Solid State Technology

Plagiarism Detection using Attention based Text Similarity

Polycystic ovary syndrome (PCOS) disorder is identified by the presence of a number of follicles present in the ovary of female reproductive system. Ultrasound imaging of the ovary contains essential information about the size, number of follicles and its position. In real time, the detection of PCOS is a difficult task for radiologists due to the various sizes of follicles and is highly connected with blood vessels and tissues. This often results in error diagnosis. For preprocessing various standard filtering techniques are applied on ovary image. Based on the performance, appropriate filter is chosen to remove the noise from the image. This paper presents an effectual active contour with modified Otsu threshold value to automated discovery of follicles from the ultrasound images. The performances of the proposed method illustrate the betterments of the proposed approach over other techniques. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Department of Digital Communications

School of Information Technology and Engineering

Multimedia Tools and Applications

Active contour with modified Otsu method for automatic detection of polycystic ovary syndrome from ultrasound image of ovary

This paper introduces Single Valued Refined Neutrosophic Set (SVRNS) which is a generalized version of the neutrosophic set. It consists of six membership functions based on imaginary and indeterminate aspect and hence, is more sensitive to real-world problems. Membership functions defined as complex (imaginary), a falsity tending towards complex and truth tending towards complex are used to handle the imaginary concept in addition to existing memberships in the Single Valued Neutrosophic Set (SVNS). Several properties of this set were also discussed. The study of imaginative pretend play of children in the age group from 1 to 10 years was taken for analysis using SVRNS, since it is a field which has an ample number of imaginary aspects involved. SVRNS will be more apt in representing these data when compared to other neutrosophic sets. Machine learning algorithms such as K-means, parallel axes coordinate, etc., were applied and visualized for a real-world application concerned with child psychology. The proposed algorithms help in analysing the mental abilities of a child on the basis of imaginative play. These algorithms aid in establishing a correlation between several determinants of imaginative play and a child’s mental abilities, and thus help in drawing logical conclusions based on it. A brief comparison of the several algorithms used is also provided.