Faculty Publications
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Publications by NITK Faculty
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Item Artificial intelligence and machine learning in battery materials and their applications(Elsevier, 2024) Acharya, S.; Viswesh, P.; Sridhar, M.K.; Pathak, A.D.; Sharma, H.; Nazir, A.; Kasbe, A.; Sahu, K.K.The fast-depleting fossil fuels and other environmental impacts necessitate rapid development and deployment of efficient, smart, intelligent, and future-ready energy storage solutions. Gone are the days of only trial-and-error-based research and development protocols that take a long time to mature and yield meaningful results, say, in discovering new structures/functional materials (nano to microstructure) for batteries or the development of new battery systems. As the existing computational power is increasing rapidly, coupled with the rapidly falling cost of computation, artificial intelligence (AI) and machine learning (ML) have proved their potential in discovering new battery materials in a short period. This chapter begins with a brief introduction to various AI and ML methods used in the development and deployment of battery material and their applications. Then we focus on the AI and ML methods used in different stages of battery production, from the material selection stage to the manufacturing, state of charge, and state of health prediction, understanding and controlling degrading and aging mechanisms and testing of battery performance, as well as some emerging AI and ML-assisted battery technologies. © 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies.Item Examining the effects of vented dams on land use and land cover in the Shambhavi Catchment: a multitemporal sentinel imagery analysis(Elsevier Ltd, 2024) Chandana, S.; Aishwarya Hegde, A.; Umesh, P.; Chandan, M.C.The rapid expansion of the global economy has given rise to concerning ecological consequences, notably a dramatic increase in land cover change (LCC). This section presents how to use the Google Earth Engine (GEE) cloud platform to explore the administrative divisions of the Southern Indian Dakshina Kannada (DK) district, which were chosen for their LCC susceptibility. Leveraging GEE, we generated a time series dataset tracking LCC over a 4-year period (2019–22). Our findings demonstrate an impressive overall accuracy (OA) of 96.30% for 2019 and 95.47% for 2022. A significant revelation in our study is the 13.64% reduction in forested areas, accompanied by a 0.68% increase in urban development within the district. This research attempt offers vital insights into the impact of dam construction on LCC, aiding informed decisions on water resource management. This research promotes a sustainable and ecologically conscious approach to holistic development in the study region and beyond. © 2024 Elsevier B.V.Item Enhancing Cybersecurity: Malicious Webpage Detection Using Machine and Deep Learning(Springer Science and Business Media Deutschland GmbH, 2025) Madhusudhan, R.; Surashe, S.V.; Pravisha, P.A wide range of techniques have been proposed for detecting malicious webpages; however, with the advent of more sophisticated webpage creation processes, it has become more challenging for these approaches to deliver satisfactory outcomes. Blacklisting and classification techniques were used in the past to identify malicious webpages. The classification of the websites becomes more challenging if they are not included on the blacklist. Machine learning techniques are gaining popularity in cybersecurity. One disadvantage of the machine learning model is that it becomes slower when using content-based features. While getting the whois feature, which gives creation, updation, and expiration dates of the webpage, the webpage is physically visited. Hence, there is a chance of malicious activity. Therefore, the process of feature extraction becomes challenging and time-consuming. This article uses the Term Frequency-Inverse Document Frequency (TF-IDF) and Natural Language Processing (NLP) methods to obtain the corpus for benign and malicious words present in the Unified Resource Locator (URL). An artificial neural network (ANN) has been employed to categorize websites as benign or malicious. A comparative analysis of artificial neural networks (ANN) with other machine learning approaches has been conducted. The experimental results demonstrate that ANN has the highest accuracy of 96.70%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Item SALR: Secure adaptive load-balancing routing in service oriented wireless sensor networks(Institute of Electrical and Electronics Engineers Inc., 2015) Lata, B.T.; Sumukha, T.V.; Suhas, H.; Tejaswi, V.; Shaila, K.; Venugopal, K.R.; Anvekar, D.; Patnaik, L.M.Congestion control and secure data transfer are the major factors that enhance the efficiency of Service Oriented Wireless Sensor Networks. It is desirable to modify the routing and security schemes adaptively in order to respond effectively to the rapidly changing Network State. Adding more complexities to the routing and security schemes increases the end-to-end delay which is not acceptable in Service Oriented WSNs which are mostly in real time. We propose an algorithm Secure Adaptive Load-Balancing Routing (SALR) protocol, in which the routing decision is taken at every hop considering the unforeseen changes in the network. Multipath selection based on Node Strength is done at every hop to decide the most secure and least congested route. The system predicts the best route rather than running the congestion detection and security schemes repeatedly. Simulation results show that security and latency performance is better than reported protocols. © 2015 IEEE.Item A composite classification model for web services based on semantic & syntactic information integration(Institute of Electrical and Electronics Engineers Inc., 2015) Kamath S․, S.; Ahmed, A.; Shankar, M.Automatic and semi-automatic approaches for classification of web services have garnered much interest due to their positive impact on tasks like service discovery, matchmaking and composition. Currently, service registries support only human classification, which results in limited recall and low precision in response to queries, due to keyword based matching. The syntactic features of a service along with certain semantics based measures used during classification can result in accurate and meaningful results. We propose an approach for web service classification based on conversion of services into a class dependent vector by applying the concept of semantic relatedness and to generate classes of services ranked by their semantic relatedness to a given query. We used the OWLS-tc service dataset for evaluating our approach and the experimental results are presented in this work. © 2015 IEEE.Item Prediction based dynamic resource provisioning in virtualized environments(Institute of Electrical and Electronics Engineers Inc., 2017) Raghunath, B.R.; Annappa, B.Dynamic provisioning to virtual machines (VMs) is one of the important requirements in the virtualized data centers to make effective utilization of resources. This can be achieved by vertical scaling or horizontal scaling of attached resources. Live virtual machine migration of virtual machines across physical machines is a vertical scaling technique which facilitates resource hotspot mitigation, server consolidation, load balancing and system level maintenance. As live migration is triggered during heavy workload (hotspot) and its procedure takes significant amount of resources to iteratively copy memory pages from source to destination, it affects the performance of other running VMs hosted on the source as well as destination physical machine (PM). Hence to avoid such performance interference effects it is necessary to trigger the migration procedure at such a point where sufficient amount of resources will be available to all the running VMs and to the migrating procedure. It is also important to select such a VM which will produce less performance interference at the source and destination. This paper presents an intelligent decision maker to trigger the migration in such a way that it avoids the said performance interference effects. It predicts the future workload for early detection of overloads and accordingly triggers the migration procedure. It also models the migration procedure to calculate performance parameters and interference parameters which are used in the decision of selection of a VM. Experimental results show that it is able to increase the performance by 45%-50% for network intensive workloads and 25%-30% for CPU, memory intensive workloads when compared with traditional method. It improves the performance by 35%-40% for network intensive workloads and 15%-20% for CPU, memory intensive workloads when compared with Sandpiper method. © 2017 IEEE.Item Automated Evaluation of Attendance and Cumulative Feedback using Face Recognition(Institute of Electrical and Electronics Engineers Inc., 2018) Shalini, S.; Navya, R.S.; Neha, M.; Ramteke, P.B.; Koolagudi, S.G.Face recognition is an important technological development of this era. It is being widely used in biometric systems, gaming as well as to tag people on social media. It is also being used for attendance because the manual system is tedious and time-consuming. This paper proposes an automated attendance and cumulative feedback system based on facial expression recognition. The proposed automation system recognizes students from a recorded video of the class and captures their attendance. Local Binary Pattern Histograms (LBPH) and Eigen Face recognizers have been used for face recognition with an accuracy of 97% and 95% respectively. This paper addresses another issue of feedback of the professor by deducing genuine and cumulative feedback based on facial expressions of the students. Two methods have been proposed for deducing the feedback. One is the algorithmic method based on face recognition using confidence measure for expressions detection and the other one uses Speeded up robust features (SURF) and Support Vector Machines(SVM). The proposed methodology is observed to be in correlation with the conventional method of feedback evaluation. Copy Right © INDIACom-2018.Item Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, M.; Sai Siri Chandana, T.; Rudra, B.Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task. © 2021 IEEE.Item Movie Box-Office Success Prediction Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2022) Velingkar, G.; Varadarajan, R.; Lanka, S.; Anand Kumar, M.Being a multi-billion dollar business, the film industry contributes largely to helping sustain a country's economy. A movie's box office (the revenue generated by the number of tickets sold of a movie) is an essential indicator of the movie's popularity. It varies depending upon several factors, including a production company, genre, budget, reviews, ratings, etc. Predicting an approximate value for a movie's box office based upon the various parameters helps investors with this business make intelligent and informed decisions. Thus, this paper designs a machine learning model that can predict the revenue a film will generate based on the information available before the movie's release. It also provides a model capable of taking in the planned genre, the required revenue, and using the Random Forest Regression model, provides recommended budget, runtime, star power, and expected popularity. © 2022 IEEE.Item Support Vector Regression based Forecasting of Solar Irradiance(Institute of Electrical and Electronics Engineers Inc., 2022) Shimpi, A.V.; Chandrasekar, A.; Keshava, A.; Vinatha Urundady, U.PV power is being increasingly popular in terms of distributed energy source and derives its energy from irradiation of the sun. This irradiation differs demographically and needs to be accurately modelled for optimizing the dispatch of the source. Many methods are already in use to forecast the sun irradiation primarily based on Neural Networks and Machine learning techniques. In this paper, Support Vector based prediction is implemented and verified on a set of data. Support Vector Regressor (SVR) is a method of shifting the data points to a hyperplane and finding the correlation between the data samples. Different Kernel functions are used to define the hyperplane and their performance compared. Various combinations of input data is used to obtain the output from the regressor. Prediction metrics are used to determine the efficacy of the algorithm and based on the metrics the worst and best models for forecasting are presented. © 2022 IEEE.