Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Selfie Detection by Synergy-Constraint Based Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2017) Annadani, Y.; Naganoor, V.; Jagadish, A.K.; Chemmangat, K.Categorisation of huge amount of data on the multimedia platform is a crucial task. In this work, we propose a novel approach to address the subtle problem of selfie detection for image database segregation on the web, given rapid rise in the number of selfies being clicked. A Convolutional Neural Network (CNN) is modeled to learn a synergy feature in the common subspace of head and shoulder orientation, derived from Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) features respectively. This synergy was captured by projecting the aforementioned features using Canonical Correlation Analysis (CCA). We show that the resulting network's convolutional activations in the neighbourhood of spatial keypoints captured by SIFT are discriminative for selfie-detection. In general, proposed approach aids in capturing intricacies present in the image data and has the potential for usage in other subtle image analysis scenarios apart from just selfie detection. We investigate and analyse the performance of the popular CNN architectures (GoogleNet, Alexnet), used for other image classification tasks, when subjected to the task of detecting the selfies on the multimedia platform. The results of the proposed approach are compared with these popular architectures on a dataset of ninety thousand images comprising of roughly equal number of selfies and non-selfies. Experimental results on this dataset shows the effectiveness of the proposed approach. © 2016 IEEE.Item Eyeball gesture controlled automatic wheelchair using deep learning(Institute of Electrical and Electronics Engineers Inc., 2018) Rajesh, A.; Mantur, M.Traditional wheelchair control is very difficult for people suffering from quadriplegia and are hence, mostly restricted to their beds. Other alternatives include Electroencephalography (EEG) based and Electrooculography (EOG) based automatic wheelchairs which use electrodes to measure neuronal activity in the brain and eye respectively. These are expensive and uncomfortable, and are almost impossible to procure for someone from a backward economy. We present a wheelchair system that can be completely controlled with eye movements and blinks that uses deep convolutional neural networks for classification. We have developed a working prototype based on only a small video camera and a microprocessor that shows upwards of 99% accuracy. We also demonstrate the significant improvement in performance over traditional image processing algorithms for the same. This will allow such patients to be more independent in their day to day lives and significantly improve quality of life at an affordable cost. © 2017 IEEE.Item SolveIt: An Application for Automated Recognition and Processing of Handwritten Mathematical Equations(Institute of Electrical and Electronics Engineers Inc., 2018) Sagar Bharadwaj, K.S.; Bhat, V.; Krishnan, A.S.Solving mathematical equations is an integral part of most, if not all forms of scientific studies. Researchers usually go through an arduous process of learning the nuances and syntactic complexities of a mathematical tool in order to solve or process mathematical equations. In this paper, we present a mobile application that can process an image of a handwritten mathematical equation captured using the device's camera, recognise the equation, form the corresponding string that can be parsed by a computer algebraic system and display all possible solutions. We aim to make the whole experience of experimenting with equations very user friendly and to remove the hassle of learning a mathematical tool just for mathematical experimentation. We propose a novel machine learning approach to recognise handwritten mathematical symbols achieving a 99.2% cross validation percentage accuracy on the kaggle math symbol dataset with reduced symbols. The application covers useful features like simultaneous equation solving, graph plotting and simple arithmetic computations from images. Overall it is a very user friendly equation solver that can leverage the power of existing powerful math packages. © 2018 IEEE.Item A Bag-of-Phonetic-Codes Modelfor Cyber-Bullying Detection in Twitter(Institute of Electrical and Electronics Engineers Inc., 2018) Shekhar, A.; Venkatesan, M.Social networking sites such as Twitter, Facebook, MySpace, Instagram are emerging as a strong medium of communication these days. These have become a part and parcel of daily life. People can express their thoughts and activities among their social circle with brings them closer to their community. However this freedom of expression has its drawbacks. Sometimes people show their aggression on Social Media which in turn hurts the sentiments of the targeted victims. Certain forms of cyber-bullying are sexual, racial and physical disability based. Hence a proper surveillance is necessary to tackle such situations. Twitter as a micro-blogging site sees cyber abuse on a daily basis. However, tweets are raw texts; containing a lot of misspelled words and censored words. This paper proposes a novel method to detect cyber-bullying, a Bag-of-Phonetic-Codes model. Using pronunciation of words as features can rectify misspelled words and can identify censored words. Correctly identifying duplicate words can lead to smaller vocabulary of words, thereby reducing the feature space. The inspiration for this proposed work is drawn from the famous Bag-of-Words model for extracting textual features. Phonetic code generation has been done using the Soundex Algorithm. Besides the proposed model, experiments were carried out with both supervised and unsupervised machine learning approaches on multiple datasets to understand the approaches and challenges in the domain of cyber-bullying detection. © 2018 IEEE.Item Deep Neural Network Models for Question Classification in Community Question-Answering Forums(Institute of Electrical and Electronics Engineers Inc., 2019) Upadhya, B.A.; Udupa, S.; Kamath S․, S.S.Automatic generation of responses to questions is a challenging problem that has applications in fields like customer support, question-answering forums etc. Prerequisite to developing such systems is a requirement for a methodology that classifies questions as yes/no or opinion-based questions, so that quick and accurate responses can be provided. Performing this classification is advantageous, as yes/no questions can generally be answered using the data that is already available. In the case of an opinion-based or a yes/no question that wasn't previously answered, an external knowledge source is needed to generate the answer. We propose a LSTM based model that performs question classification into the two aforementioned categories. Given a question as an input, the objective is to classify it into opinion-based or yes/no question. The proposed model was tested on the Amazon community question-answer dataset as it is reflective of the problem statement we are trying to solve. The proposed methodology achieved promising results, with a high accuracy rate of 91% in question classification. © 2019 IEEE.Item Image Based Tomato Leaf Disease Detection(Institute of Electrical and Electronics Engineers Inc., 2019) Kumar, A.; Vani, M.Leaf diseases are the major problem in agricultural sector, which affects crop production as well as economic profit. Early detection of diseases using deep learning could avoid such a disaster. Currently, Convolutional Neural Network (CNN) is a class of deep learning which is widely used for the image classification task. We have performed experiments with the CNN architecture for detecting disease in tomato leaves. We trained a deep convolutional neural network using PlantVillage dataset of 14,903 images of diseased and healthy plant leaves, to identify the type of leaves. The trained model achieves test accuracy of 99.25%. © 2019 IEEE.Item Automated Parking System in Smart Campus Using Computer Vision Technique(Institute of Electrical and Electronics Engineers Inc., 2019) Banerjee, S.; Ashwin, T.S.; Guddeti, R.M.R.In today's world we need to maintain safety and security of the people around us. So we need to have a well connected surveillance system for keeping active information of various locations according to our needs. A real-time object detection is very important for many applications such as traffic monitoring, classroom monitoring, security rescue, and parking system. From past decade, Convolutional Neural Networks is evolved as a powerful models for recognizing images and videos and it is widely used in the computer vision related work for the best and most used approach for different problem scenario related to object detection and localization. In this work, we have proposed a deep convolutional network architecture to automate the parking system in smart campus with modified Single-shot Multibox Detector (SSD) approach. Further, we created our dataset to train and test the proposed computer vision technique. The experimental results demonstrated an accuracy of 71.2% for the created dataset. © 2019 IEEE.Item Deep neural learning for automated diagnostic code group prediction using unstructured nursing notes(Association for Computing Machinery, 2020) Jayasimha, A.; Gangavarapu, T.; Kamath S․, S.; S. Krishnan, G.S.Disease prediction, a central problem in clinical care and management, has gained much significance over the last decade. Nursing notes documented by caregivers contain valuable information concerning a patient's state, which can aid in the development of intelligent clinical prediction systems. Moreover, due to the limited adaptation of structured electronic health records in developing countries, the need for disease prediction from such clinical text has garnered substantial interest from the research community. The availability of large, publicly available databases such as MIMIC-III, and advancements in machine and deep learning models with high predictive capabilities have further facilitated research in this direction. In this work, we model the latent knowledge embedded in the unstructured clinical nursing notes, to address the clinical task of disease prediction as a multi-label classification of ICD-9 code groups. We present EnTAGS, which facilitates aggregation of the data in the clinical nursing notes of a patient, by modeling them independent of one another. To handle the sparsity and high dimensionality of clinical nursing notes effectively, our proposed EnTAGS is built on the topics extracted using Non-negative matrix factorization. Furthermore, we explore the applicability of deep learning models for the clinical task of disease prediction, and assess the reliability of the proposed models using standard evaluation metrics. Our experimental evaluation revealed that the proposed approach consistently exceeded the state-of-the-art prediction model by 1.87% in accuracy, 12.68% in AUPRC, and 11.64% in MCC score. © 2020 Association for Computing Machinery.Item Motion Deblurring of Faces(Institute of Electrical and Electronics Engineers Inc., 2020) Anand, P.; Sumam David, S.; Sudeep, K.S.This paper evaluates learning-based data-driven models for deblurring of facial images. Existing algorithms for deblurring, when used for facial images, often fail to preserve the facial shape and identity information. The best available models, which are used for general-purpose image deblurring, are pre-trained using only facial images. The Peak Signal to Noise Ratio (PSNR) Structural Similarity Index Measure (SSIM) and Time to deblur single images are the key metrics used for evaluating the models and for finding the most efficient model for deblurring facial images. From the results, the observation is that even though the PSNR value for DeblurGANv2 model is the highest, the best trade off between PSNR, SSIM, Time to deblur and visual quality is seen in DeblurGAN model. © 2020 IEEE.Item Empirical Study on Multi Convolutional Layer-based Convolutional Neural Network Classifier for Plant Leaf Disease Detection(Institute of Electrical and Electronics Engineers Inc., 2020) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Recognizing the plant disease automatically in real-time by examining a plant leaf image is highly essential for farmers. This work focuses on an empirical study on Multi Convolutional Layer-based Convolutional Neural Network (MCLCNN) classifier to measure the detection efficacy of MCLCNN on recognizing plant leaf image as being healthy or diseased. To achieve this, a set of experiments were conducted with three distinct plant leaf datasets. Each of the experiments were conducted by setting kernel size of 3× 3 and each experiment was conducted independently with different epochs i.e., 50, 75, 100, 125, and 150. The MCLCNN classifier achieved minimum accuracy of 87.47% with 50 epochs and maximum accuracy of 99.25% with 150 epochs for the Peach plant leaves. © 2020 IEEE.