Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A comparative study of different auto-focus methods for mycobacterium tuberculosis detection from brightfield microscopic images(Institute of Electrical and Electronics Engineers Inc., 2016) Saini, G.; Panicker, R.O.; Soman, B.; Rajan, J.Automatic tuberculosis (TB) detection methods using microscopic images are becoming more popular now a days. Auto-focusing is the first and foremost step in the development of an automated microscope for TB detection. Different focus measures exist for the selection of in-focus image from both fluorescence and bright field microscopic images. Recently, some researchers have investigated and compared several different focus measures for TB sputum microscopy. In this study we focused on bright field microscopic images and considered around 20 popular focus measures. Experiments were conducted on a large set of images having different features. © 2016 IEEE.Item Follow Me: A Human Following Robot Using Wi-Fi Received Signal Strength Indicator(Springer Science and Business Media Deutschland GmbH, 2021) Geetha, V.; Salvi, S.; Saini, G.; Yadav, N.; Singh Tomar, R.P.Modernization has brought up significant changes in the way humans used to communicate with devices which aid in reducing strenuous work. Robotics has made life simpler by automating some specific tasks, one such application of robotics is explored in this paper. Human Following Robot (HFR) can be used to carry heavy goods or simply follow its user to act as an interface between IoT connected devices. In this paper, a Low-Cost Protoytpe of Human Follower Robot is proposed which uses Received Signal Strength Indicator of Wi-Fi to establish its task. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation(Springer Science and Business Media Deutschland GmbH, 2021) Saini, G.; Yadav, N.; Mohan, B.R.; Naik, N.In this paper we are going to discuss the prediction of the financial time series using the Markov chain changing transition matrix model using genetic algorithm. During initial phase of the algorithm we will create the window of fix size with fixed number of state. The basic aim of this paper is to reduce the time taken to find the best window size and best number of states in the window by using the genetic algorithm. This paper produce the approach so that investor can save their time to predict the series without manual activity. To demonstrate the genetic algorithm optimisation we used the historical index data: national stock exchange(NSE50). The Nifty data contained 1239 candles starting from January 1,2015 and ending December 31, 2019. Data was downloaded from [ https://www1.nseindia.com/ ]. In this case we observed the better investment strategy using the first order Markov chain model and reducing the execution time by using the genetic algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item CNN-GRU: Transforming image into sentence using GRU and attention mechanism(Grenze Scientific Society, 2021) Saini, G.; Patil, N.Recent advancement of the deep neural network has triggered great attention in both Natural Language Processing (NLP) and Computer Vision (CV). It provides an efficient way of understanding semantic and syntactic structure which can deal with complex task such as automatic image captioning. Image captioning methodology mainly based on the encoder-decoder approach. In the present work, we developed a CNN-GRU model using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and attention mechanism. Here VGG16 is used as an encoder, GRU and attention mechanism are used as a decoder. Our model has shown significant improvement compared to other state-of-art encoder-decoder models on the famous MSCOCO data set. Further, the time taken to train and test our model is two-third as compared to other similar models such as CNN-CNN and CNN-RNN. © Grenze Scientific Society, 2021.Item Ensemble Neural Models for Depressive Tendency Prediction Based on Social Media Activity of Twitter Users(Springer Science and Business Media Deutschland GmbH, 2022) Saini, G.; Yadav, N.; Kamath S․, S.In view of the ongoing pandemic, Clinical Depression (CD) is a serious health challenge for a large segment of the population. According to recent public surveys, more than 30 million American citizens are the victim of depression each year and depression also causes 30 thousand suicides each year. Early detection of depression can help provide much needed medical intervention and treatment for better mental health. Toward this, the social media posts of users can be a significant source for analyzing their mental health signals, and can also serve as a measure for assessing the prevalence of clinical depression tendencies in the population. In this paper, an approach that leverages the predictive power of supervised and semi-supervised learning algorithms for detecting depressive tendencies in the population using social media activity is presented. Learning models were trained on preprocessed tweet data from the Sentiment140 dataset containing 1.6 million labeled tweets. We also designed a convolution neural network model for the prediction task that outperformed machine learning models by a significant margin with an accuracy of 97.1%. The performance of the proposed models is benchmarked using standard metrics like SMDI (Social Media Depression Index). Crowd-sourcing approaches were adopted for collecting real-time social behavior of users to train the proposed model and demonstrate its potential for real-world applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.