Conference Papers
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Item SVM based methods for arrhythmia classification in ECG(2010) Kohli, N.; Verma, N.K.; Roy, A.In this study, Support Vector Machine (SVM) based methods have been used to classify the electrocardiogram (ECG) arrhythmias. Among various existing SVM methods, three well-known and widely used algorithms one-against-one, one-against-all, and fuzzy decision function are used here to distinguish between the presence and absence of cardiac arrhythmia and classifying them into one of the arrhythmia groups. The various types of arrhythmias in the Cardiac Arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM, include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. The results obtained through implementation of all three methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier using one-against-all (OAA) method was found to be better than other techniques. ECG arrhythmia data sets are of generally complex nature and SVM based one-against-all method is found to be of vital importance for classification based diagnosing diseases pertaining to abnormal heart beats. ©2010 IEEE.Item Syntactic and semantic feature extraction and preprocessing to reduce noise in bug classification(2012) Agrawal, R.; Guddeti, G.In software industry a lot of effort is spent in analyzing the bug report to classify the bugs. This Classification helps in assigning the bugs to the specific team for Bug Fixing according to the nature of the bug. In this paper, we have proposed a data mining technique applying syntactic and semantic Feature Extraction to assist developers in bug Classification. Extracted features are organized into different feature groups then a specific preprocessing technique is applied to each feature group. The applied methods have reduced the noise in the bug data compared to traditional approach of word frequency for text categorization. We have analyzed our approach on a collection of bug reports collected from a networking based organization (CISCO).The experiments are performed using Naive Bayes Multinomial Model and Support Vector Machine on features obtained after preprocessing. © 2012 Springer-Verlag.Item Classification of tidal inlets along the central west coast of India(Elsevier Ltd, 2015) Mendi, M.; Reddy, N.A.; Rao, S.; Seelam, J.K.Tidal inlets along the Maharashtra coast on the central west coast of India were classified according to three methods available in the literature. Two classification methods viz., (i) Hydrodynamic classification (Hayes, 1979) and (ii) Classification based on dimensionless parameters (Vu, 2013) used for the classification are compared with the morphological classification of de Vriend et al., (1999). The hydrodynamic classification of Tidal inlets along Maharashtra coast is carried out considering mean annual significant wave height. The classification is also extended considering significant wave heights obtained for South-West monsoon, North-East monsoon and Fair Weather seasons. It has been observed that 74% of the inlets are tide dominated as per morphological classification whereas considering annual mean wave heights in Vu (2013) method, 67% of the inlets are wave dominated. © 2015 The Authors. Published by Elsevier Ltd.Item Identification of allied raagas in Carnatic music(Institute of Electrical and Electronics Engineers Inc., 2015) Upadhyaya, P.; Suma, S.M.; Koolagudi, S.G.In this work, an effort has been made to differentiate the allied raagas in Carnatic music. Allied raagas are the raagas that are composed using same set of notes. The features derived from the pitch sequence are used for differentiating these raagas. The coefficients of legendre polynomials, used to fit the pitch contours of the song clips are used for identifying raagas. Obtained features are validated using different classifiers such as Neural networks, Naive Bayes, Multi class classifier, Bagging and Random forest. The proposed system is tested on 4 sets of allied raagas. Naive Bayes classifier gives an average accuracy of 86.67% for allied set of Todi-Dhanyasi and Multi class classifier gives an average accuracy of 86.67% for allied set of Kharaharapriya-Anandabhairavi-Reethigoula. In general, Neural network classifier performance is found to be better than other classifiers. © 2015 IEEE.Item Classification of case-II waters using hyperspectral (HICO) data over North Indian Ocean(SPIE spie@spie.org, 2016) Srinivasa Rao, N.; Ramarao, E.P.; Srinivas, K.; Deka, P.C.State of the art Ocean color algorithms are proven for retrieving the ocean constituents (chlorophyll-a, CDOM and Suspended Sediments) in case-I waters. However, these algorithms could not perform well at case-II waters because of the optical complexity. Hyperspectral data is found to be promising to classify the case-II waters. The aim of this study is to propose the spectral bands for future Ocean color sensors to classify the case-II waters. Study has been performed with Rrs's of HICO at estuaries of the river Indus and GBM of North Indian Ocean. Appropriate field samples are not available to validate and propose empirical models to retrieve concentrations. The sensor HICO is not currently operational to plan validation exercise. Aqua MODIS data at case-I and Case-II waters are used as complementary to in- situ. Analysis of Spectral reflectance curves suggests the band ratios of Rrs 484 nm and Rrs 581 nm, Rrs 490 nm and Rrs 426 nm to classify the Chlorophyll -a and CDOM respectively. Rrs 610 nm gives the best scope for suspended sediment retrieval. The work suggests the need for ocean color sensors with central wavelength's of 426, 484, 490, 581 and 610 nm to estimate the concentrations of Chl-a, Suspended Sediments and CDOM in case-II waters. © 2016 SPIE.Item Smartphone based emotion recognition and classification(Institute of Electrical and Electronics Engineers Inc., 2017) Sneha, H.R.; Rafi, M.; Manoj Kumar, M.V.; Thomas, L.; Annappa, B.This paper proposes a method that classifies the emotion status of a human being based on one's interactions with the smart phone. Due to one or the other practical limitations, the existing set of emotion recognition methods are difficult to use on daily basis (most of the known methods cause inconvenience to user since they require devices like wearable sensors, camera, or answering a questionnaire). The essence of this paper is to analyze the textual content of the message and user typing behavior to build a classifier that efficiently classifies the future instances. Each observation in the data set consists of 14 features. A machine learning technique called Naive Bayes classifier is applied to construct the classifier. Method proposed is capable of classifying emotions in one of the seven classes (anger, disgust, happy, sad, neutral, surprised, and fear). Experimental result has shown 72% accuracy in classification. © 2017 IEEE.Item A New Islanding Detection Method Using Transfer Learning Technique(IEEE Computer Society help@computer.org, 2018) Manikonda, S.K.G.; Gaonkar, D.N.The increasing need for energy in the recent times is unprecedented, which is driving the penetration of renewable sources in distribution system in a big way. The increasing number of renewable sources in a system has made the operation, control and protection of the system very complex. One of the key issues in seamless interconnection of renewable energy sources to a system is islanding. This paper proposes a new method to detect islanding in an efficient way by employing transfer learning based technique for image classification. The results show that the proposed method can successfully classify islanding events with a good accuracy. © 2018 IEEE.Item Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India(Springer Verlag service@springer.de, 2019) Saicharan, S.; Saha, M.; Mitra, P.; Nanjundiah, R.S.Monsoon spells are important climatic phenomenon modulating the quality and quantity of monsoon over a year. India being an agricultural country, identification of monsoon spells is extremely important to plan agricultural policies following the phases of monsoon to attain maximum productivity. Monsoon spells’ detection involve analyzing and predicting monsoon at daily levels which make it more challenging as daily-variability is higher as compared to monsoon over a month or an year. In this article, deep-learning based long short-term memory and sequence-to-sequence models are utilized to classify monsoon days, which are finally assembled to detect the spells. Dry and wet days are classified with precision of 0.95 and 0.87, respectively. Break spells are observed to be forecast with higher accuracy than the active spells. Additionally, sequence-to-sequence model is noted to perform superior to that of long-short term memory model. The proposed models also outperform traditional classification models for monsoon spell detection. © Springer Nature Switzerland AG 2019.Item A Novel Islanding Detection Method Based on Transfer Learning Technique Using VGG16 Network(Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Gaonkar, D.N.The escalating need for energy in the recent times is unprecedented, which is driving the penetration of renewable energy sources in distribution system in a big way. The growing number of renewable sources in a system has made the control, operation and protection of the system very complex. Among others, one of the key issues in seamless interconnection of renewable energy sources to a system is islanding. This paper proposes a new and efficient islanding detection method that employs transfer learning based technique. The results show that the proposed method can successfully classify islanding events with a good accuracy. © 2019 IEEE.Item Power Quality Event Classification Using Transfer Learning on Images(Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Santhosh, J.; Sreckala, S.P.K.; Gangwani, S.; Gaonkar, D.N.Given the ever-increasing complexity of the electrical grid system, power quality events have been surging in frequency with each passing day. Due to their potential to cause massive losses for a wide variety of customers, it is crucial that such events are detected and classified immediately for appropriate response. in this paper, a novel approach has been developed wherein Transfer Learning techniques have been employed to classify power quality events using image classification. More specifically, the VGG16 model has been utilized to classify five distinct power quality issues by using scalograms as input images. 489 scalograms were generated via feature extraction using wavelet transforms. The VGG16 model has then been trained and tested using the same. Thereafter, the model performance has been evaluated, and the results have been discussed. © 2019 IEEE.