Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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 Loss Optimised Video Captioning using Deep-LSTM, Attention Mechanism and Weighted Loss Metrices(Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, N.; Naik, D.The aim of the video captioning task is to use multiple natural-language sentences to define video content. Photographic, graphical, and auditory data are all used in the videos. Our goal is to investigate and recognize the video's visual features, as well as to create a caption so that anyone can get the video's information within a second. Despite the fact, that phase encoder-decoder models have made significant progress, but it still needs many improvements. In the present work, we enhanced the top-down architecture using Bahdanau Attention, Deep-Long Short-Term Memory (Deep-LSTM) and weighted loss function. VGG16 is used to extract the features from the frames. To understand the actions in the video, Deep-LSTM is paired with an attention system. On the MSVD dataset, we analysed the efficiency of our model, which indicates a major improvement over the other state-of-art model. © 2021 IEEE.Item Generating Short Video Description using Deep-LSTM and Attention Mechanism(Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, N.; Naik, D.In modern days, extensive amount of data is produced from videos, because most of the populations have video capturing devices such as mobile phone, camera, etc. The video comprises of photographic data, textual data, and auditory data. Our aim is to investigate and recognize the visual feature of the video and to generate the caption so that users can get the information of the video in an instant of time. Many technologies capture static content of the frame but for video captioning, dynamic information is more important compared to static information. In this work, we introduced an Encoder-Decoder architecture using Deep-Long Short-Term Memory (Deep-LSTM) and Bahdanau Attention. In the encoder, Convolution Neural Network (CNN) VGG16 and Deep-LSTM are used for deducing information from frames and Deep-LSTM combined with attention mechanism for describing action performed in the video. We evaluated the performance of our model on MSVD dataset, which shows significant improvement as compared to the other video captioning model. © 2021 IEEE.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.