Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
2 results
Search Results
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 Isolated Kannada Character Recognition using Densely Connected Convolutional Network(Institute of Electrical and Electronics Engineers Inc., 2022) Sandhya, S.; Geetha, V.Handwritten Character Recognition and Identification are one of the most interesting problem statements in the present digitized world because of its variety of applications. It has leveraged its potential in reducing the manual work of converting the documents containing handwritten characters to machine-readable texts. Recognition of handwritten characters is challenging due to various reasons like high variance in the writing styles across the globe, poor quality of the handwritten text compared to the printed text and the size of the handwritten text. Kannada language has a history of over 1000 years. Kannada vowels and consonants are curvy and symmetric in nature and hence recognition in an offline system becomes difficult. Hence, recognition of Handwritten Kannada characters effectively serves as the main objective of this work. This work proposes a DenseNet121 based Character Recognition model that effectively recognizes the Handwritten Kannada characters. Transfer Learning is used to improve the overall performance of the model. The proposed model achieved a training accuracy of 96.7% and test accuracy of 96.28%, hence proving the effectiveness of the model. © 2022 IEEE.