Incorporating automatically predicted human feedback into the process of training generative models has attracted substantial recent interest, while feedback at inference time has received less attention. The typical ...
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The Learning Management System (LMS) is an essential tool for educational institutions that facilitates content delivery, assessments, lecture delivery, and collaboration to enhance the learning experience. This study...
The Learning Management System (LMS) is an essential tool for educational institutions that facilitates content delivery, assessments, lecture delivery, and collaboration to enhance the learning experience. This study explores the role of LMS in creating an effective learning environment to improve students’ academic performance. To achieve the main objective of this study, we utilized a dataset [xAPI-Edu-Data] comprising multiple factors, such as academic, psychological, and cognitive engagement. Various machine learning techniques are employed to assess the impact of engagement activities on students’ performance. Initially, a class imbalance issue identified in the dataset and addressed using SMOTE technique. In addition, other resampling strategies applied to compare the effectiveness of proposed work. The model performance evaluated and compared using different evaluation metrics before and after data enrichment. In addition, hyperparameter optimization is conducted using a grid search approach to enhance models’ accuracy. The performance of individual models such as support vector machine (0.81), logistic regression (0.80), and decision tree (0.75) enhanced using the enriched dataset. The integration of multiple base learners into an ensemble model, with random forest as the stacking learner, achieved a weighted precision of 0.83, improving from 0.60 with the original dataset. The implementation of the stacking approach with enriched dataset has identified a better result and improved accuracy by 23%. The key contribution of this study includes identifying the effectiveness of data enrichment in improving prediction accuracy. Moreover, the research highlights the role of student engagement and behavior in measuring academic performance. The proposed model can identify the factors behind low performance, allowing further actions to be taken. Based on the prediction, the educators can work on the associated factors that could be low engagement, participation, or
Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-...
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Trusted Execution Environments (TEEs) isolate a special space within a device’s memory that is not accessible to the normal world (also known as Untrusted Environment), even when the device is compromised. Thus, deve...
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Cataract surgery, a widely performed operation worldwide, is incorporating semantic segmentation to advance computer-assisted intervention. However, the tissue appearance and illumination in cataract surgery often dif...
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We propose a threshold password-hardening updatable oblivious key management system dubbed TPH-UOKM for cloud storage. In TPH-UOKM, a group of key servers share a user-specific secret key for a user, and assist the us...
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This paper introduces a framework for capturing stochasticity of choice probabilities in neural networks, derived from and fully consistent with the Random Utility Maximization (RUM) theory, referred to as RUM-NN. Neu...
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Manufacturers offer adjustable control parameters for flight control systems to accommodate diverse environments and missions. To ensure flight safety, they also develop established boundaries, i.e., range specificati...
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Accurate prediction and analysis of travel mode choices are critical for effective transportation planning and have garnered significant attention in research. Traditionally, travel mode choice prediction has been con...
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