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Item HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction(Institute of Electrical and Electronics Engineers Inc., 2025) Das, M.; Mohan, B.R.; Guddeti, R.M.R.This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper designed two novel hybrid swarm-optimization algorithms (SOAs) referred to as gravitational force grasshopper optimization algorithm-artificial bee colony (GFGOA-ABC), and levy flight grasshopper optimization algorithm-artificial bee colony (LFGOA-ABC) algorithms. By combining the enhanced exploration features of LFGOA and GFGOA with the robust exploitation capacity of the artificial bee colony (ABC), the LFGOA-ABC and GFGOA-ABC algorithms are proposed. Prior to validating the HSoMLSDP framework, the LFGOA-ABC and GFGOA-ABC algorithm’s efficacy is first confirmed by experimenting on 19 benchmark functions (BFs) to assess their mean, standard deviation (SD) of optimal values, convergence rate, and convergence rate improvements. Following BFs verification, the second experiment tunes the hyperparameters of the ML models (artificial neural network, XGBOOST) to improve the defect accuracy of the SoMLDP model. The outcomes of the experiments justify a more rapid convergence rate for BFs and notable enhancements of 0.01-0.28 in software defect prediction (SDP) accuracy for NASA defect datasets when compared with state-of-the-art methods. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework. © 2013 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.