Conference Papers
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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.Item Deep Learning Based Smart Garbage Monitoring System(Institute of Electrical and Electronics Engineers Inc., 2020) Rao, P.P.; Rao, S.P.; Ranjan, R.India has witnessed an unprecedented increase in garbage levels in the past 20 years. Massive quantities of waste, particularly solid waste, are generated daily and seldom picked up. Consequently, garbage is being dumped in landfills and water bodies, hence not managed effectively. This mismanagement has detrimental consequences on our environment. Thus, there is a need to develop an efficient system to manage waste. In this paper, an IoT-based, automated smart bin monitoring system is proposed. Moreover, a deep learning model was used to forecast future garbage levels from the data collected. The proposed neural network model was able to predict garbage levels with an accuracy of 80.33%. Results verify the accurate prognosis of garbage levels. Additionally, data were analysed using bar charts. The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbagebins. © 2020 IEEE.Item Detecting Semantic Similarity of Documents Using Natural Language Processing(Elsevier B.V., 2021) Agarwala, S.; Anagawadi, A.; Reddy Guddeti, R.M.The similarity of documents in natural languages can be judged based on how similar the embeddings corresponding to their textual content are. Embeddings capture the lexical and semantic information of texts, and they can be obtained through bag-of-words approaches using the embeddings of constituent words or through pre-trained encoders. This paper examines various existing approaches to obtain embeddings from texts, which is then used to detect similarity between them. A novel model which builds upon the Universal Sentence Encoder is also developed to do the same. The explored models are tested on the SICK-dataset, and the correlation between the ground truth values given in the dataset and the predicted similarity is computed using the Pearson, Spearman and Kendall's Tau correlation metrics. Experimental results demonstrate that the novel model outperforms the existing approaches. Finally, an application is developed using the novel model to detect semantic similarity between a set of documents. © 2021 Elsevier B.V.. All rights reserved.Item Human Activity Recognition in Smart Home using Deep Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2021) Kolkar, R.; Geetha, V.To understand the human activities and anticipate his intentions Human Activity Recognition(HAR) research is rapidly developing in tandem with the widespread availability of sensors. Various applications like elderly care and health monitoring systems in smart homes use smartphones and wearable devices. This paper proposes an effective HAR framework that uses deep learning methodology like Convolution Neural Networks(CNN), variations of LSTM(Long Short term Memory) and Gated Recurrent Units(GRU) Networks to recognize the activities based on smartphone sensors. The hybrid use of CNN-LSTM eliminates the handcrafted feature engineering and uses spatial and temporal data deep. The experiments are carried on UCI HAR and WISDM data sets, and the comparison results are obtained. The result shows a better 96.83 % and 98.00% for the UCI-HAR and WISDM datasets, respectively. © 2021 IEEE.Item Long Short Term Memory Networks for Lexical Normalization of Tweets(Institute of Electrical and Electronics Engineers Inc., 2021) Nayak, P.; Praueeth, G.; Kulkarni, R.; Anand Kumar, M.Lexical normalization is converting a non-standard text into a standard text that is more readable and universal. Data obtained from social media sites and tweets often contain much noise and use non-canonical sentence structures such as non-standard abbrevlatlons, skipping of words, spelling errors, etc. Hence such data needs to be appropriately processed before it can be used. The processing can be done by lexical normalization, which reduces randomness and converts the sentence structure to a predefined standard. Hence. lexical normalization can help in improving the performance of systems that use user-generated text as inputs. There are several ways to perform lexical normalization, such as dictionary lookups, most frequent replacements, etc. However, VVe aim to explore the domain of deep learning to find approaches that can be used to normalize texts lexically. © 2021 IEEE.Item Deep Learning Techniques for Artistic Image Transformations: A Survey(Institute of Electrical and Electronics Engineers Inc., 2021) Aralikatti, R.C.; Sangeeth, S.V.; Chandavarkar, B.R.Deep learning has greatly revolutionized the ways in which computers tackle problems in vision, speech recognition, machine translation, etc., and has produced results which are almost inconceivable to conventional algorithms. Creative tasks such as fine arts and music composition, which were initially thought to be impossible to computers, are now possible. In this paper, we look at a particular class of problems called image-to-image translation problems and see how it can be leveraged to perform artistic image transformations. Generative Adversarial Networks (GANs) and related neural networks are particularly useful for this task. We explore some of the artistic image transformation tasks that deep learning can be used for and discuss the different machine learning architectures used, the results produced and the advancements made in literature towards tackling such tasks. © 2021 IEEE.Item Deployment of Computer Vision Application on Edge Platform(Institute of Electrical and Electronics Engineers Inc., 2021) Geetha, V.; Kiran, C.; Sharma, M.; Rakshith Kumar, J.In our work, we propose a low cost device which will aid visually impaired people to understand what is in their surroundings without the requirement of internet. Current technology makes use of Cloud Architecture and would require internet to achieve this purpose. But these systems will not work in areas with poor internet connectivity. Edge platform built on Raspberry Pi powered with Intel Neural Compute Stick is used by us for this purpose. Multi Label Image Classification Deep Learning Model is trained in the cloud. It is later optimised and deployed on Edge Device which is Raspberry Pi. Setup also consists of PiCamera which will record the video and give it as input to deployed model. Model will describe the items present in video, basically describing the surroundings. The output is in the form of audio which is played through speakers, thus enabling visually impaired people to understand their surroundings without the requirement of internet. Deployment of popular Machine Learning and Deep Learning Models is also examined in the edge device and a comprehensive performance evaluation is performed. © 2021 IEEE.Item Data Processing in IoT, Sensor to Cloud: Survey(Institute of Electrical and Electronics Engineers Inc., 2021) Sandeep, M.; Chandavarkar, B.R.IoT is connecting Things over the Internet and the realization of the environment through smart things to create a responsive space. Many surveys predicted the growth of IoT devices is going to be around 50 billion and an average of 7 devices per person. IoT has shown promising future with its applications like smart city, connected factories, buildings, roadways, smart health and many more. To make the promise a reality IoT has to overcome many hurdles like scalability, connectivity, architectural, big data, analysis, security, and privacy. In this literature survey, an attempt has been made to identify current challenges faced by IoT implementation and possible solutions, future opportunities, and research openings. Further, the processing of sensed data at IoT device, edge/fog layer, and the cloud is discussed in detail. © 2021 IEEE.