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

The wide-ranging implementation of the digital Internet of Things (IoT) system in recent years has contributed to the development of smart cities. In real-world time, smart cities are designed to encourage simplicity and quality of life in developed areas. A smart city’s network traffic from loT networks is increasingly growing and posing new cybersecurity problems, because these loT devices are linked to sensors that are directly connected to large cloud servers. The researchers need to refine new methods for identifying compromised loT machines to prevent such cyberattacks. In the smart networks, traditional protection strategies are cumbersome to implement because of complexity in communication systems, vendor regulations, requirements, technology and location-specific resources. To address these difficulties, we used a Probabilistic Timed Automaton (PTA) to model the operating actions of smart devices and introduced novel Time Dependent Anomaly Detection Systems (TDADS) utilizing the operational behaviour of smart home environment. Simulations to test our concept are performed in real time. It is clear from the simulation findings that our TDADS achieves effective usage of resources and robust packet transport. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

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Journal of Ambient Intelligence and Humanized Computing

Time dependent anomaly detection system for smart environment using probabilistic timed automaton

This study aims to analyse the sensitivity in decision-making which results in the selection of the appropriate underground metal mining method using the fuzzy-analytical hierarchy process (FAHP) model. The proposed model considers sixteen criteria for the selection of the most appropriate mining method out of the seven. The model consists of three-layer viz. the first layer represents the criteria (factors which influence the mining method), the second layer represents the sub-criteria (categorisation of the factors) and the third layer represents the alternatives (mining methods). The priority of the different mining methods was determined based on global weights. The global weights of seven mining method were determined using a different fuzzification factor under different decision-making attitudes (optimistic, pessimistic and unbiased). The sensitivity of the decision-making results was analysed in order to understand the robustness of the model. © 2019

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Journal of Sustainable Mining

Sensitivity analysis of fuzzy-analytic hierarchical process (FAHP) decision-making model in selection of underground metal mining method

The present study aims to make a comparative study of the selection of mining method using five multi-criteria decision-making (MCDM) models (TOPSIS, VIKOR, improved ELECTRE, PROMETHEE II, and WPM). Underground mining method selection is a multi-criteria decision-making problem, and the mine planners face the challenges in the selection of the appropriate mining method. The selection of mining method depends on multiple intrinsic factors (dip, shape, thickness, depth, grade distribution, RMR of ore, RMR of hanging wall, and RMR of footwall) and extrinsic factors (available technology). The study considered only intrinsic factors in selection of mining method. In the last few decades, many multi-criteria decision-making models have been developed. The study uses AHP technique for determining the weights of the effective criteria. The proposed techniques were implemented for Tummalapalle mine of Uranium Corporation of India Limited (UCIL), India. The results revealed that mining methods selected were not uniform. Actually it is a case of room and pillar being the preferred method by three of the MCDM models, while it is a second or equal preference method in two of the MCDM models applied. © 2018, The Institution of Engineers (India).

Journal of The Institution of Engineers (India): Series D

A Comparative Study of Various Multi-criteria Decision-Making Models in Underground Mining Method Selection

The present study aims to design a machine vision system using deep learning algorithm for quality monitoring of iron ores. A total of 53 image samples were used for model calibration and testing. The model was trained using 45 image samples and tested using 9 image samples. The model parameters like the number of nodes and number layers were optimized based on the Root Mean Squared Error (RMSE) values. It was observed that the RMSE was lowest for the network architecture having 5-nodes and 3-hidden layers. The performance of the optimized model was evaluated using four indices including RMSE, Normalized Mean Square Error (NMSE), R-squared, and bias. The RMSE, NMSE, R-squared, and bias of the optimized model were obtained as 8.77, 0.0026, 0.87, and −1.14 respectively. The results indicate that the model gives satisfactory performance in quality predictions of iron ores. © 2019 Taylor & Francis Group, London.

Mining Goes Digital - Proceedings of the 39th international symposium on Application of Computers and Operations Research in the Mineral Industry, APCOM 2019

A deep learning approach for automated quality control of iron ores

The aim of this paper is to represent the solution to the problem of selecting suitable underground mining method for the mining industry. It is achieved by using two multi-attribute decision making techniques. These two techniques are weighted product method (WPM) and preference ranking organization method for enrichment evaluation (PROMETHEE). In this paper, analytic hierarchy process is used for weight’s calculation of the attributes (i.e. parameters which are used in this paper). Mining method selection depends on physical parameters, mechanical parameters, economical parameters and technical parameters. WPM and PROMETHEE techniques have the ability to consider the relationship between the parameters and mining methods. The proposed techniques give higher accuracy and faster computation capability when compared with other decision making techniques. The proposed techniques are presented to determine the effective mining method for bauxite mine. The results of these techniques are compared with methods used in the earlier research works. The results show, conventional cut and fill method is the most suitable mining method. © 2017, The Institution of Engineers (India).

Underground Mining Method Selection Using WPM and PROMETHEE

Decision making is a process of selecting the best alternative from the pool of alternatives. This selection process depends on many influencing parameters; these parameters may be beneficiary or non-beneficiary. The proposed decision-making techniques were implemented in the appropriate underground mining method selection process. The selection of underground mining method depends on various geo-mining parameters such as technical, physical, mechanical, and economic parameters. In the proposed work, the selection of best mining method for bauxite deposit was implemented using Improved AHP and VIKOR. Improved AHP technique was considered to determine the weights of the influencing parameters. The proposed techniques can consider the association among the influencing parameters and alternatives. Results obtained by the proposed techniques were compared with the results obtained by other researchers for the bauxite deposit. The results showed that the suitable mining method for the specified criteria of the bauxite mine was conventional cut and fill. © Springer Nature Singapore Pte Ltd. 2018.

Advances in Intelligent Systems and Computing

Design of decision-making techniques using improved AHP and VIKOR for selection of underground mining method

The aim of the current study is to develop a multi-criteria decision making model using fuzzy-analytical hierarchical process (FAHP) for selection of the appropriate underground metal mining method. The model considered eight criteria for evaluating the seven mining methods. A three-level hierarchical structured model is proposed for decision-making analysis. The criteria, sub-criteria, and the mining methods are listed respectively in the first, second, and third level of the hierarchy. The local and global weights for each parameter were determined using the FAHP method. The global weights of the various mining methods were used to determine the ranking of various mining method for a typical ore deposit. The model validation was done using the ore deposit data of two mines in India. It is observed that the adopted mining methods and the corresponding predicted mining methods are same in both the cases. Copyright © 2018 Inderscience Enterprises Ltd.