Faculty Publications
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Publications by NITK Faculty
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Item A gene expression based quality of service aware routing protocol for mobile ad hoc networks(2013) Kubusada, Y.; Mohan, G.; Manjappa, M.; Guddeti, G.Mobile Ad Hoc Network (MANET) is a collection of infrastructure less multi-hop wireless mobile nodes which communicate together to achieve the global task. Despite lack of centralized control these mobile nodes still coordinate together to deliver the message to the destination node. MANET is gaining its popularity due to its easy deployment and self-organizing ability. In spite of its unique characteristics, mobility of mobile nodes causes frequent link breakups in MANET and thus makes route setup and maintenance a critical and challenging task. As real time and multimedia applications are increasing, there is a need of an efficient Quality of Service (QoS) aware routing protocol for MANET to support such applications. In the present work, the authors proposed an efficient QoS aware routing protocol for MANET based on upcoming Gene Expression Programming. In the proposed work, the information regarding the availability of resources is managed by a resource management module, which assists in selecting the resource rich path. Further, a theoretical proof is given for the proposed model for its correctness. The results are compared with the state of art artificial neural network and support vector regression methods from the performance evaluation point of view and the results are encouraging. © 2013 Springer Science+Business Media.Item Classification of vocal and non-vocal regions from audio songs using spectral features and pitch variations(Institute of Electrical and Electronics Engineers Inc., 2015) Vishnu Srinivasa Murthy, Y.V.S.; Koolagudi, S.G.In this work, an effort has been made to identify vocal and non-vocal regions from a given song using signal processing techniques and machine learning algorithm. Initially spectral features like mel-frequency cepstral coefficients (MFCCs) are used to develop the baseline system. Statistical values of pitch, jitter and shimmer are considered to improve performance of the system. Artificial neural networks (ANNs) are used to capture the characteristics of vocal and non-vocal segments of the songs. The experiment is conducted on 60 vocal and 60 non-vocal clips extracted from Telugu albums. 11-point moving window is used to ensure the continuity of vocal and non-vocal segments, thus improving the accuracy of system. With this approach system achieves 85.59% accuracy for vocal and 88.52% for non-vocal segment classification. © 2015 IEEE.Item Audio songs classification based on music patterns(Springer Verlag service@springer.de, 2016) Sharma, R.; Vishnu Srinivasa Murthy, Y.V.S.; Koolagudi, S.G.In this work, effort has been made to classify audio songs based on their music pattern which helps us to retrieve the music clips based on listener’s taste. This task is helpful in indexing and accessing the music clip based on listener’s state. Seven main categories are considered for this work such as devotional, energetic, folk, happy, pleasant, sad and, sleepy. Forty music clips of each category for training phase and fifteen clips of each category for testing phase are considered; vibrato-related features such as jitter and shimmer along with the mel-frequency cepstral coefficients (MFCCs); statistical values of pitch such as min, max, mean, and standard deviation are computed and added to the MFCCs, jitter, and shimmer which results in a 19-dimensional feature vector. feedforward backpropagation neural network (BPNN) is used as a classifier due to its efficiency in mapping the nonlinear relations. The accuracy of 82% is achieved on an average for 105 testing clips. © Springer India 2016.Item Sound event detection in urban soundscape using two-level classification(Institute of Electrical and Electronics Engineers Inc., 2016) Luitel, B.; Vishnu Srinivasa Murthy, Y.V.S.; Koolagudi, S.G.A huge increase in automobile field h as lead t o the creation of different sounds in large volume, especially in urban cities. An analysis of the increased quantity of automobiles will give information related to traffic and vehicles. It also provides a scope to understand the scenario of particular location using sound scape information. In this paper, a two level classification is proposed to classify urban sound events such as bus engine (BE), bus horn (BH), car horn (CH) and whistle (W) sounds. The above sounds are taken as they place a major role in traffic scenario. A real-time data is collected from the live recordings at major locations of the urban city. Prior to the detection of events, the class of events is identified u sing signal processing techniques. Further, features such as Mel-frequency cepstral coefficients (MFCCs) a re extracted based on the analysis of a spectrum of the above-mentioned events and they are prominent to classify even in the complex scenario. Classifiers such as artificial neural networks (ANN), naive-Bayesian (NB), decision tree (J48), random forest (RF) are used at two levels. The proposed approach outperforms the existing approaches that usually does direct feature extraction without signal level analysis. © 2016 IEEE.Item Modeling hybrid indicators for stock index prediction(Springer Verlag service@springer.de, 2020) Arjun, R.; Suprabha, K.R.The study aims to assess the major predictors of stock index closing using select set of technical and fundamental indicators from market data. Here two of major service sector specific indices of Bombay stock exchange (BSE) and National stock exchange (NSE) with historical data from 2004 up to 2016 are considered. By experimental simulation, the predictive estimates of index closing using automatic linear modeling, time-series based forecasting, and also artificial neural network models are analyzed. While linear models show better performance for BSE, artificial neural network based models exhibit higher predictive modeling accuracy for NSE. The design aspects are outlined for augmenting intelligent market prediction systems. © Springer Nature Switzerland AG 2020.Item Current approaches of artificial intelligence in breakwaters - A review(Techno Press technop2@chollian.net, 2017) Kundapura, S.; Hegde, A.V.A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables. © 2017 Techno-Press, Ltd.Item Prediction of bod and cod of a refinery wastewater using multilayer artificial neural networks(Universidade Federal da Paraiba, 2008) Rene, E.R.; Saidutta, M.B.In the recent past, artificial neural networks (ANNs) have shown the ability to learn and capture non-linear static or dynamic behaviour among variables based on the given set of data. Since the knowledge of internal procedure is not necessary, the modelling can take place with minimum previous knowledge about the process through proper training of the network. In the present study, 12 ANN based models were proposed to predict the Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) concentrations of wastewater generated from the effluent treatment plant of a petrochemical industry. By employing the standard back error propagation (BEP) algorithm, the network was trained with 103 data points for water quality indices such as Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Phenol concentration, Ammoniacal Nitrogen (AMN), Total Organic Carbon (TOC) and Kjeldahl's Nitrogen (KJN) to predict BOD and COD. After appropriate training, the network was tested with a separate test data and the best model was chosen based on the sum square error (training) and percentage average relative error (% ARE for testing). The results from this study reveal that ANNs can be accurate and efficacious in predicting unknown concentrations of water quality parameters through its versatile training process. © 2008 Journal of Urban and Environmental Engineering (JUEE). All rights reserved.Item Artificial neural networks model for the prediction of steady state phenol biodegradation in a pulsed plate bioreactor(2008) Shetty K, K.V.; Nandennavar, S.; Srinikethan, G.Background: A recent innovation in fixed film bioreactors is the pulsed plate bioreactor (PPBR) with immobilized cells. The successful development of a theoretical model for this reactor relies on the knowledge of several parameters, which may vary with the process conditions. It may also be a time-consuming and costly task because of their nonlinear nature. Artificial neural networks (ANN) offer the potential of a generic approach to the modeling of nonlinear systems. Results: A feedforward ANN based model for the prediction of steady state percentage degradation of phenol in a PPBR by immobilized cells of Nocardia hydrocarbonoxydans (NCIM 2386) during continuous biodegradation has been developed to correlate the steady state percentage degradation with the flow rate, influent phenol concentration and vibrational velocity (amplitude x frequency). The model used two hidden layers and 53 parameters (weights and biases). The network model was then compared with a Multiple Regression Analysis (MRA) model, derived from the same training data. Further these two models were used to predict the percentage degradation of phenol for blind test data. Conclusions: The performance of the ANN model was superior to that of the MRA model and was found to be an efficient data-driven tool to predict the performance of a PPBR for phenol biodegradation. © 2008 Society of Chemical Industry.Item Detection of bradycardia from electrocardiogram signals using feature extraction and snapshot ensembling(Springer Science and Business Media B.V., 2022) Sengupta, S.; Mayya, V.; Kamath S․, S.K.One of the most common diagnostic techniques for detecting certain cardiovascular diseases is using electrocardiogram (ECG) readings. Doctors around the world mostly rely on human insight and processing to determine and interpret these ECG graphs. This process is thus often prone to human error introduced to the increasing cognitive burden of doctors and might introduce delays in diagnosis, which could be fatal. Ongoing research has focused on the design of automated algorithms to accurately diagnose and speed up the process of analyzing and interpreting an ECG signal. In this paper, we present a novel approach that utilizes a neural network pipeline with Snapshot ensembling to enable automated Bradycardia detection from ECG signals. Before the modeling phase, a cross-correlation and segmentation method is used for detecting relevant features in the ECG signals, using which the detection performance is improved. The proposed approach gave good results, with around 95% accuracy and an AUC score of about 0.96, implying an efficient and accurate classification. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.