Faculty Publications
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Item Data reduction by removal of lurkers in OSN(2013) Sumith, S.; Annappa, B.; Bhattacharya, S.With the advent of internet, online social network is seen as playing a very important role in connecting people and a platform to share ideas. In the current scenario, given restriction on resources available for advertisement, the best place to sell one's product would definitely be these online social networks. The popularity of social network has influenced the computer researchers to ponder on the question on who are the people playing vital roles in information spread. This paper reviews the state of art work done previously in estimation of influence in Online Social Network(OSN) and proposes an innovative idea to improve the existing influence estimation algorithm in terms of search space and runtime. © 2013 IEEE.Item Analysis of Pollution Level Near Mining Area using Neural Network(Grenze Scientific Society, 2024) Bantwal, R.; Vittal, K.P.The curve fitting analysis is performed on time-stamped signal values. Then a situational analysis graphical user interface (GUI) is developed to analyze the data which is from a mining area. The resulting waveform plots and the designed GUI are presented. The data is from the mining area namely Singrauli. Five environmental parameters related to pollution, namely CO, SO2, PM10, PM2.5, and Ozone are considered for the analysis using neural network. © Grenze Scientific Society, 2024.Item Physico-mechanical properties of select granitoidal rocks from a part of Pandiyan mobile belt, India(2012) Sivapragasam, C.; Venkat Reddy, D.; Kulandaisamy, K.; Vigneswaran, M.; Senthilkumaran, S.; Sivaprasath, C.; Varun Kumar, M.This study deals with the Physico-mechanical properties of the granites from the Pandiyan mobile belt to identify the suitability of the granite for engineering purposes. The geological characteristics, the mining procedure and mineralogical and engineering properties of the granite samples are estimated through laboratory tests. Based on the results, it is concluded that granitoids of Madurai region is best suited for all the civil engineering works. The granites in Sankarankovil region are more suited as aggregates for road pavements. © 2012 Cafet-Innova Technical Society.Item An economic analysis of environmental pollution and health - a case study of Bellary-Hospet sector(2012) Thimmaiah, S.A.; Ravi, D.R.; Rao, Y.V.; Murthy, C.S.N.The Earth's natural ecosystem is the basis for our life-supporting system and provides marketable goods to human and other living organisms. The natural environment has always been exploited to fulfill human needs. The green revolution and industrial revolution has caused serious threat to sustainable development for both developed and developing countries. The degradation of air, water and land has directly affected the livelihood and human health. The environmental damage increases, as the economic activity increases, in view of the fact that the association between economy and the environment are multiple, complex and important. Mining is one such activity, which significantly results in the degradation of the environment, apart from generating huge economy to the country. Hence, the identification and quantification of socio economic impact of environmental pollution caused due to increased mining activity is necessary in the broader economic analysis. The present study is envisaged with the objective to identify and evaluate the impacts of mining activity on Social, Economical and Environmental Aspects of the area and to measure its economic burden on the affected people. The ambient air quality in the selected stations of study area reveals that, the increase in iron ore production has significantly resulted in the deterioration of air quality. High particulate matter to an extent of 310 ?g/nm3 in case of SPM concentration and 160 ?g/nm3 in case of RSPM. The health data collected from the respondents have revealed, significantly more number of respondents who are suffering from dust allergy, skin allergy in the study area, where there are mines and are working as workers in those mines, contributing for higher health cost incurred through treatment. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item Portable low cost drill set-up for estimating rock properties(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Masood; Vardhan, H.; Mangalpady, M.The mechanical strength of rock is one of the most important factors of concern to engineers involved in mining operations. Information about rock strength is used in rock excavation planning and design operations in civil and mining engineering. Drilling is widely carried out in hard rock’s for blasting the rock mass so that the blasted material can be easily loaded by the excavators. The drillability of rock depends on many factors including rock properties. Whereas properties such as compressive strength, porosity, density etc. are uncontrollable parameters during drilling process. A number of studies have been reported recently on the application of sound level, which have been concentrated on using either CNC or jack hammer machine for drilling purpose. It is worth mentioning that neither CNC machine nor jack hammer drill set-up is the normal way of drilling in rock, nor in mining, civil or any other operations, not even in working with rock in installation of countertops. Therefore, it is difficult to exactly say whether the noise generated during drilling is only from the rock drilling or from the drilling unit itself. In view of the above, it is important to fabricate a new drilling set-up which is a silent unit in itself. Such unit when used for drilling purpose will clearly indicate the change in sound level produced with different rock properties. © 2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Discovering suspicious behavior in multilayer social networks(Elsevier Ltd, 2017) Bindu, P.V.; Santhi Thilagam, P.S.; Ahuja, D.Discovering suspicious and illicit behavior in social networks is a significant problem in social network analysis. The patterns of interactions of suspicious users are quite different from their peers and can be identified by using anomaly detection techniques. The existing anomaly detection techniques on social networks focus on networks with only one type of interaction among the users. However, human interactions are inherently multiplex in nature with multiple types of relationships existing among the users, leading to the formation of multilayer social networks. In this paper, we investigate the problem of anomaly detection on multilayer social networks by combining the rich information available in multiple network layers. We propose a pioneer approach namely ADOMS (Anomaly Detection On Multilayer Social networks), an unsupervised, parameter-free, and network feature-based methodology, that automatically detects anomalous users in a multilayer social network and rank them according to their anomalousness. We consider the two well-known anomalous patterns of clique/near-clique and star/near-star anomalies in social networks, and users are ranked according to the degree of similarity of their neighborhoods in different layers to stars or cliques. Experimental results on several real-world multilayer network datasets demonstrate that our approach can effectively detect anomalous nodes in multilayer social networks. © 2017 Elsevier LtdItem Genetic characterization of sulphur and iron oxidizing bacteria in manganese mining area of Balaghat and Chhindwara, Madhya Pradesh, India(National Institute of Science Communication and Information Resources (NISCAIR) ijact.editor@gmail.com Dr. K. S. Krishnan Marg (Near Pusa Gate) New Delhi 110-012, 2018) Dixit, S.J.; Appu Kuttan, K.K.; Shrivastava, R.M.The aim of present study was to explore microbiology of manganese mining area of Balaghat and Chhindwara, Madhya Pradesh, India with the objective of reducing load of mine based pollution to support environmental sustainability with help of bacterial isolates. The research involves physicochemical analysis, culture dependent methods, 16S rDNA based sequencing and computational phylogenetic analysis. The 16S rDNA sequence analysis revealed the occurrence of two iron oxidizing bacteria (Staphylococcus hominis and Pseudomonas sp.) and four sulphur oxidizing bacteria (Bacillus cereus HYM74, B. anthracis, B. cereus D42 and Pantoea calida) in the selected sites. All cultures were able to grow on acidic as well as neutral pH medium and at low temperature of 4oC. Bacterial isolates were also found with heavy metal tolerance for Mn+7 and Cr+6 up to the concentration of 1000 ppm. This study assists the idea of biomineralization, bioremediation and future reclamation in the selected mining area with the help of bacteria. © 2018 National Institute of Science Communication and Information Resources (NISCAIR).All Rights Reserved.Item Modelling stream flow and soil erosion response considering varied land practices in a cascading river basin(Academic Press, 2020) Venkatesh, K.; Ramesh, H.; Das, P.[No abstract available]Item Risk factors associated with work-related musculoskeletal disorders among dumper operators: A machine learning approach(Elsevier B.V., 2023) Kar, M.B.; Mangalpady, M.; Kunar, B.M.Aims: This study aimed to determine the risk factors associated with work-related musculoskeletal disorders (WRMSDs) among dumper operators working in Indian iron ore mines. Methods: A total of 246 dumper truck operators meeting inclusion and exclusion criteria were chosen for data collection. A self-report custom and the standard Nordic questionnaire were used for collecting data about risk factors and WRMSDs. The data were pre-processed and analyzed using machine learning (ML) algorithms (such as logistic regression ( LR), support vector machines (SVM), decision trees (DT), gradient boosting machine (GBM) and random forest (RF)). Results: RF model was found to outperform the other algorithms with high accuracy (0.71), precision (0.75), recall (0.78), F1 score (0.76), and area under the receiver operating characteristic curve (0.82). The mean rank of the risk factors showed that age is the most critical parameter, followed by awkward posture, experience in mines, job demand, alcohol consumption, smoking cigarettes, work design, and marriage status. Conclusion: Overall, the study provides valuable insights into the risk factors associated with WRMSDs among dumper operators and suggests that measures should be taken to address these risk factors to prevent WRMSDs in the dumper operator population. © 2023 The Author(s)Item Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques(Nature Research, 2024) Tripathi, A.K.; Mangalpady, M.; Parida, S.; Durgesh Nandan, D.; Elumalai, P.V.; Prakash, E.; Joshua Ramesh Lalvani, J.S.C.; Koppula, K.S.The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models—Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)—for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment. © The Author(s) 2024.