Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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 Multiclass SVM-based language-independent emotion recognition using selective speech features(Institute of Electrical and Electronics Engineers Inc., 2014) Kokane Amol, T.; Guddeti, G.R.M.In this paper, we emphasize on recognizing six basic emotions viz. Anger, Disgust, Fear, Happiness, Neutral and Sadness using selective features of speech signal of different languages like Germen and Telugu. The feature set includes thirteen Mel-Frequency Cepstral Coefficients (MFCC) and four other features of speech signal such as Energy, Short Term Energy, Spectral Roll-Off and Zero-Crossing Rate (ZCR). The Surrey Audio-Visual Expressed Emotion (SAVEE) Database is used to train the Multiclass Support Vector Machine (SVM) classifier and a German Corpus EMO-DB (Berlin Database of Emotional Speech) and Telugu Corpus IITKGP: SESC are used for emotion recognition. The results are analyzed for each speech emotion separately and obtained accuracies of 98.3071% and 95.8166 % for Emo-DB, IITKGP: SESC databases respectively. © 2014 IEEE.Item Product review based on optimized facial expression detection(Institute of Electrical and Electronics Engineers Inc., 2017) Chaugule, V.; Abhishek, D.; Vijayakumar, A.; Ramteke, P.B.; Koolagudi, S.G.This paper proposes a method to review public acceptance of products based on their brand by analyzing the facial expression of the customer intending to buy the product from a supermarket or hypermarket. In such cases, facial expression recognition plays a significant role in product review. Here, facial expression detection is performed by extracting feature points using a modified Harris algorithm. The modified Harris algorithm reduced the time complexity of the existing feature extraction Harris Algorithm. A comparison of time complexities of existing algorithms is done with proposed algorithm. The algorithm proved to be significantly faster and nearly accurate for the needed application by reducing the time complexity for corner points detection. © 2016 IEEE.Item Video Affective Content Analysis based on multimodal features using a novel hybrid SVM-RBM classifier(Institute of Electrical and Electronics Engineers Inc., 2017) Ashwin, T.S.; Saran, S.; Guddeti, G.R.M.Video Affective Content Analysis is an active research area in computer vision. Live Streaming video has become one of the modes of communication in the recent decade. Hence video affect content analysis plays a vital role. Existing works on video affective content analysis are more focused on predicting the current state of the users using either of the visual or the acoustic features. In this paper, we propose a novel hybrid SVM-RBM classifier which recognizes the emotion for both live streaming video and stored video data using audio-visual features; thus recognizes the users' mood based on categorical emotion descriptors. The proposed method is experimented for human emotions recognition for live streaming data using the devices such as Microsoft Kinect and Web Cam. Further we tested and validated using standard datasets like HUMANE and SAVEE. Classification of emotion is performed for both acoustic and visual data using Restricted Boltzmann Machine (RBM) and Support Vector Machine (SVM). It is observed that SVM-RBM classifier outperforms RBM and SVM for annotated datasets. © 2016 IEEE.Item Power Quality Event Classification Using Long Short-Term Memory Networks(Institute of Electrical and Electronics Engineers Inc., 2019) Manikonda, S.K.G.; Santhosh, J.; Sreckala, S.P.K.; Gangwani, S.; Gaonkar, D.N.Due to the increased frequency of power quality events and complexity of modern electric grids, there is a growing need to classify such events. In this paper, a novel approach to the above problem has been explored, wherein Long Short-Term Memory networks have been employed to fulfil the power quality event classification task. Given the sheer size of the input dataset, feature extraction was carried out by deriving important statistical features from the data. The Long Short-Term Memory model used was then trained and tested on these extracted features. Following this, the model performance has been evaluated, wherein the model was shown to perform remarkably well. © 2019 IEEE.Item Automated Parking Lot Management System Using Number Plate Recognition(Institute of Electrical and Electronics Engineers Inc., 2025) Nagar, R.; Suresha, S.N.; Bairwa, B.In modern era, due to the rapid growth of vehicles had led to a significant increase in parking spaces in areas such as work spaces, business parks etc. Parking management has become a tenacious task to carry out. Wherein, this research paper proposes an Automated parking lot management system using a state-of-the art technology, which is Number Plate Recognition. This Automated parking lot management system using number plate recognition utilizes a Scale Invariant Feature Transform (SIFT) algorithm which aims in providing accurate detection and recognition of the vehicle's number plate. This SIFT algorithm is implemented using MATLAB Simulink. A collection of 136 vehicles dataset is taken to validate and assist system's performance. This system identifies and matches to the vehicle's number plate present in the dataset by using the app camera. When a match is detected, permission is granted for the vehicle to access the parking facility. In the absence of a match, the system impedes the vehicle and prompts the owner to settle the parking fee. The primary aim of this mechanism is to oversee and regulate the ingress and egress of vehicles in the parking area through the utilization of Number Plate Recognition technology. © 2025 IEEE.