Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
2 results
Search Results
Item Modulation and signal class labelling with active learning and classification using machine learning(Institute of Electrical and Electronics Engineers Inc., 2022) Bhargava, B.C.; Deshmukh, A.; Narasimhadhan, A.V.Supervised learning in machine learning (ML) requires labelled data set. Further real-time data classification requires an easily available methodology for labelling. Wireless modulation and signal classification find their application in plenty of areas such as military, commercial and electronic reconnaissance and cognitive radio. This paper mainly aims to solve the problem of real-time wireless modulation and signal class labelling with an active learning framework. Further modulation and signal classification is performed with machine learning algorithms such as KNN, SVM, Naive Bayes. Active learning helps in labelling the data points belonging to different classes with the least amount of data samples trained. An accuracy of 86 percent is obtained by the active learning algorithm for the signal with SNR 18 dB. Further, KNN based model for modulation and signal classification perform well over a range of SNR, and an accuracy of 99.8 percent is obtained for 18 dB signal. The novelty of this work exists in applying active learning for wireless modulation and signal class labelling. Both modulation and signal classes are labelled at a given time with help of couplet formation from the data samples. © 2022 IEEE.Item Federated learning approach for human activity recognition in online examination environment(Springer, 2025) Ramu, S.; Guddeti, R.M.R.; Mohan, B.R.In recent years, online exams have become a key method for assessing students’ knowledge and skills. However, with the rise of e-learning, conducting these exams has introduced new challenges, especially due to the increasing tendency of students to engage in cheating during online assessments. To address this, student activities during online exams are monitored to detect behaviors that indicate cheating through Human Activity Recognition (HAR). HAR is a system capable of recognizing various human activities based on observational data. This study focuses on detecting student behavior during online exams, categorizing normal activities as non-cheating and abnormal activities as potential cheating or malpractice. For this purpose, a federated learning architecture was utilized to process online exam data. In this approach, we implemented federated models, including Federated-ResNet50, Federated-DenseNet121, Federated-VGG16, and Federated-CNN, to classify student activities. A OEP dataset is utilized in this work comprising of various activities such as using mobile devices, copying from notes, abnormal head gaze and normal. Model performance for classification was evaluated using accuracy, precision, recall and F1-Score metrics. The results were compared across the federated models, namely Federated-ResNet50, Federated-DenseNet121, Federated-VGG16, and Federated-CNN. Among these, Federated-ResNet50 performed the best, achieving an accuracy of 91.28%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.