Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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.Item Composite Power System Reliability Evaluation Using Artificial Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2023) Yarramsetty, C.; Moger, T.; Jena, D.This paper uses Deep learning and Monte Carlo Simulation (MCS) to speed up composite power system reliability evaluation. Due to recurring optimum power flow (OPF) solutions, reliability evaluation approaches for large integrated power grids are computationally demanding. Machine learning can avoid OPF in reliability assessment by identifying system states as successful or failed. They can only assess energy and power indices by solving OPF for all failures. This research calculates minimal load curtailments without OPF, except during training. Power, energy, probability, and frequency indices are evaluated. This paper presents a neural network based classification technique and linear regression based regression for evaluating composite power systems reliability. The proposed framework is illustrated through IEEE RTS 79 and 96 test systems. Findings show that the proposed method calculates composite system reliability indices accurately and efficiently. © 2023 IEEE.Item Advancements in Financial Document Structure Extraction: Insights from Five Years of FinTOC (2019-2023)(Institute of Electrical and Electronics Engineers Inc., 2023) Kang, J.; Patel, M.M.; Agrawal, A.; Simhadri, S.; Srinivasa, R.; Bellato, S.; Anand Kumar, M.; Tsang, N.D.; El-Haj, M.In this comprehensive paper, we present a detailed overview of the Financial Table Of Content extraction shared task series, FinTOC, conducted over a span of five years from 2019 to 2023. This paper serves as a retrospective analysis of the key developments in the field of financial document structure extraction. The FinTOC series, hosted within the framework of the Financial Narrative Processing (FNP) workshop, has been instrumental in shaping the landscape of Natural Language Processing (NLP) in the financial domain. Our analysis delves into the diverse methodologies proposed by participants across all editions, shedding light on the innovative strategies employed to tackle the intricate challenge of extracting structured information from financial documents. We explore the evolution of techniques, from traditional rule-based approaches to cutting-edge deep learning models, showcasing the dynamic nature of NLP advancements. Furthermore, our study investigates the introduction of multilingual datasets by the organizers, highlighting the importance of cross-lingual analysis in financial document processing. We also examine the contributions made by participants in augmenting the training data with external sources, showcasing the collaborative spirit of the NLP community in enhancing the quality and size of the shared training dataset. © 2023 IEEE.Item Leveraging Deep Learning for Fever Temperature Analysis and Pattern Recognition(Institute of Electrical and Electronics Engineers Inc., 2024) Prabhakaran, A.; Sumam David, S.; Vijayasenan, D.; Mahabala, C.; Dakappa, P.Tympanic temperature is one of the most fundamental indicators for the diagnosis of diseases. Due to its importance, using patients' temperature data to aid in the diagnostic process would be beneficial. This work uses temperature data collected from various patients to classify diseases. We consider dengue, tuberculosis, and non-infectious and non-tubercular bacterial diseases. Extracting essential features from the temperature data is necessary so that the downstream layers only have to consider important features, not miscellaneous information. This feature extraction is done using two methods - Convolution Neural Networks and Autoencoders. We introduce three models for Explainable Temperature Analysis - ExTemp-Conv-SM, ExTemp-Conv-LG and ExTemp-Auto. We achieve a classification accuracy of 70% over these four disease classes. We also use explainable AI tools, like GradCAM, to identify distinguishing patterns in temperature fluctuations that can characterize diseases. We generate such patterns for all four diseases under consideration. We note that the patterns generated for dengue and tuberculosis match the findings in biological observation studies. We hope that the methods in this paper can be leveraged for other diseases and used to aid the diagnostic process. © 2024 IEEE.