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 Software Systems

Autonomous drones play a vital role in Disaster mitigation systems and commercial good delivery systems. The problem involves finding the shortest path between the delivery points while simultaneously avoiding stationary obstacles (for example high raised buildings) and moving obstacles like other drones. The path needs to be continuously changed based on the telemetry from other drones or based on the addition of new way-points. This is major issue in planning problems. Any algorithm will have to make complex choices like abandoning shortest paths to avoid collisions. In this paper we propose a tangent algorithm which chooses paths based on many performance measures like number of obstacles in current path and the future path and the distance to the next obstacle. The path has very few sharp turns and the locations of these turns are calculated during the path planning. The performance evaluation on different environments demonstrates that the algorithm will be particularly faster in case of both sparse and dense obstacles. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

World University root node

School of Computer Science and Engineering

Vellore Campus

Advances in Intelligent Systems and Computing

Recursive tangent algorithm for path planning in autonomous systems

VIT Unitversity

Fulltext

International Journal of Engineering Research and

Real Time Bus Tracking System

IOP Conference Series: Materials Science and Engineering

Augmented reality for SCADA

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.

Department of Information Security

Neural Computing and Applications

Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization

Heating load and cooling load forecasting are crucial for estimating energy consumption and improvement of energy performance during the design phase of buildings. Since the capacity of cooling ventilation and air-conditioning system of the building contributes to the operation cost, it is ideal to develop accurate models for heating and cooling load forecasting of buildings. This paper proposes a machine-learning technique for prediction of heating load and cooling load of residential buildings. The proposed model is deep neural network (DNN), which presents a category of learning algorithms that adopt nonlinear extraction of information in several steps within a hierarchical framework, primarily applied for learning and pattern classification. The output of DNN has been compared with other proposed methods such as gradient boosted machine (GBM), Gaussian process regression (GPR) and minimax probability machine regression (MPMR). To develop DNN model, the energy data set has been divided into training (70%) and testing (30%) sets. The performance of proposed model was benchmarked by statistical performance metrics such as variance accounted for (VAF), relative average absolute error (RAAE), root means absolute error (RMAE), coefficient of determination (R2), standard deviation ratio (RSR), mean absolute percentage error (MAPE), Nash–Sutcliffe coefficient (NS), root means squared error (RMSE), weighted mean absolute percent error (WMAPE) and mean absolute percentage Error (MAPE). DNN and GPR have produced best-predicted VAF for cooling load and heating load of 99.76% and 99.84% respectively. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

Forecasting heating and cooling loads of buildings: a comparative performance analysis

Department of Integrative Biology

School of Bio Sciences and Technology

Department of Bio-Medical Sciences

International Journal of Diabetes in Developing Countries

Administration of coenzyme Q10 to a diabetic rat model: changes in biochemical, antioxidant, and histopathological indicators

In the recent times of data transmission, applying the Internet of Things (IoT) device, networking to the various IoT modules is still a great challenge with the deployment stage. Very few localities in the world have the deployed IoT module integrated in its communication, but still maximum of the location are in testbed. As a practical application, the IoT modules can be used to collect the information about either the drunken person who is driving the vehicle or the heavily loaded vehicles. The vehicle that we are about to search should be integrated with the IoT module. In this paper, a novel approach for object tracking specifically the drunken driving person and the heavily loaded vehicle is proposed to ensure the follow of rules and prevent any mis happening. The task is to identify the exact vehicle which does not follow the rules which can be tracked and eliminated. For that we are proposing a simple methodology on identifying the vehicle by the tracking the locality of the vehicle as a cluster of nodes where the cluster provides the key inference on how the vehicles are interrelated in the learning environment.

Indonesian Journal of Electrical Engineering and Computer Science

Identification of mobile vehicle through multilayer intercommunication

Journal of Molecular Liquids

Transient numerical analysis of thermophoresis and particle dynamics in a nanofluid – pool boiling conditions

Body mass index (BMI) is used widely as an indicator in general health. Determination of BMI using non-intrusive measurements are of interest and recent advancements in the availability of digital imaging sensors have paved the way for performing quick and automatic measurements. In this work, we consider automatic computation of BMI using correlation features from face images. We show that using face detection based facial fiducial points analysis provides good BMI prediction. Experimental results on comparing the correlation coefficients of facial ratios along with the colour feature has higher significance in BMI of a person. © 2020 - IOS Press and the authors. All rights reserved.

Technology and Health Care

Automatic body mass index detection using correlation of face visual cues

Evolutionary Intelligence

A cognitive mechanism for mitigating DDoS attacks using the artificial immune system in a cloud environment