Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Analysis and Prediction of Fantasy Cricket Contest Winners Using Machine Learning Techniques(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Karthik, K.; S. Krishnan, G.S.; Shetty, S.; Bankapur, S.; Kolkar, R.; Ashwin, T.S.; Vanahalli, M.K.Cricket is one of the well-known sports across the world. The increasing interest of cricket in recent years resulted in different forms like T20, T10 from test and one day format. The craze of all these formats of cricket matches today has come into online fantasy cricket league games. Dream11 is one such app that is most popular in this context, along with many similar apps. Creating a dream team of 11 players from playing 11 of both teams involves skills, ideas and luck. Predicting a winner among all the joined contestants based on the previous historical data is a challenging task. In this paper, we used a feed-forward deep neural network (DNN) classifier for predicting the winning contestant for the top three positions in a fantasy league cricket contest. The performance of the DNN approach was compared against that of state-of-the-art machine learning approaches like k-nearest neighbours (KNN), logistic regression (LR), Naive Bayes (NB), random forest (RF), support vector machines (SVM) and in predicting the fantasy cricket contest winners. Among the methods used, DNN showed the best results for all three positions, showing its consistency in predicting the winners and outperforms the state-of-the-art machine learning classifiers by 13%, 8% and 9%, respectively, for first, second and third winning positions, respectively. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.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 IoT-based Human Activity Recognition Models based on CNN, LSTM and GRU(Institute of Electrical and Electronics Engineers Inc., 2022) Kolkar, R.; Singh Tomar, R.P.; Vasantha, G.Smartphones' ability to generate data with their inbuilt sensors has made them used for Human Activity Recognition. The work highlights the importance of Human Activity Recognition (HAR) systems capable of sensing human activities like the inertial motion of a human body. The sensors are worn on a body part and tracked from whole-body motions and monitoring. Real-time signal processing is used to sense human body movements using wearable sensors. The work aims to provide opportunities for promising health applications using IoT. There are many challenges to recognising human activities, including accuracy. This work analyses Human Activity recognition concerning CNN, LSTM, and GRU deep learning models to improve the accuracy of the human activity recognition in the UCI-HAR and WISDM datasets. The comparative analysis shows promising results for Human activity recognition. © 2022 IEEE.Item Online Video Stabilization using Mesh Flow with Minimum Latency(Institute of Electrical and Electronics Engineers Inc., 2023) Devaguptam, D.; Thanmai, K.; Raj, L.S.; Naik, D.; Kolkar, R.Most existing video stabilization techniques are used for post-processing, where previously recorded videos are given to the model to obtain stabilized versions. Online video stabilization usually relies on sensors like gyroscopes or assumes constant motion, which is not suitable for videos with changing motions. This work introduces a video stabilization technique with just one-frame latency. The algorithm operates at the spatial level in the infrequent domain, tracking the motion of mesh vertices. Motion tracks of feature marks are combined with the nearest mesh vertex using two median gauges, assigning each vertex a smooth motion track. The proposed approach, called anticipated foster track leveling, smoothes the motion profiles by utilizing previous motions and adapting accordingly for smoother results. This method can handle changes in movement in space and time and works in real-time, allowing applications in security systems, robotics, and unmanned aerial vehicles (UAVs). When evaluated against other models, MeshFlow gives an overall good performance in all comparison metrics evaluated. Hence MeshFlow can be used as a reliable low-latency technique for real-time video stabilization in remote devices. © 2023 IEEE.Item Single Person Occupancy Detection Using PIR Sensors(Springer Science and Business Media Deutschland GmbH, 2024) Kolkar, R.; Geetha, V.; Salvi, S.The occupancy detection system presented in this study utilizes a combination of two PIR sensors and a micro-controller board to detect and store occupancy information in different rooms accurately. The PIR sensors detect motion within their field of view while the micro-controller processes the sensor inputs and controls the storage of occupancy data in a memory device. The circuit provides real-time occupancy status updates and allows for data retrieval for further analysis. The setup offers significant advantages such as energy efficiency, simplicity, and cost-effectiveness. The experimental results demonstrate the system's effectiveness in accurately detecting and storing occupancy information. The results show the elderly spend time in various rooms. The combined circuit has potential applications in various domains, including smart homes, energy management, and security systems, where knowledge of room occupancy patterns is crucial for optimizing resources and enhancing user experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Li-Fi for Secured Access to Wireless Network During Online Examination in Classrooms(Springer Science and Business Media Deutschland GmbH, 2024) Salvi, S.; Apune, S.; Gutte, V.; Gawade, M.; Kolkar, R.The traditional conduction of examinations needs to use physical copies of question paper and answer sheets, which are then evaluated by the evaluators. However, in a post-pandemic era, where the emphasis is more on the minimal usage of physically transferable materials, safe conduction of examinations in the classroom environments becomes challenging. Conduction of examination in complete online mode requires wireless access to the Wi-Fi access points; however, as the range of the Wi-Fi access point goes beyond the classrooms, there is the possibility of accessing the network from outside classroom which is not desired. With an aim to address these issues, in this paper a novel approach for providing dynamically changing passwords using visible light communication is designed, implemented and tested for connecting to the wireless network. The setup is useful in the environment where restricted physical access is needed to ensure system and network security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.