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This paper aims to determine how the LMS Web portal application reshapes the learner experience through the developed E-Learning Management System using Data Mining Algorithm. The methodology that the researchers used is descriptive…
Integrating quantum computing into deep learning architectures is a promising but poorly understood endeavor: when does a quantum layer actually help, and how much quantum is enough? We address both questions through Quantum Adaptive…
Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and accurate. To tackle this issue and cater to the learning…
The success of e-Learning is largely dependent on the impact of its multimedia aided learning content on the learner over the hyper media. The e-Learning portals with different proportion of multimedia elements have different impact on the…
Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc. However, despite decades of research, they remain far from entering an era of…
Recent advances in large language models (LLMs) have enabled intelligent tutoring systems, yet the development of LLM-based Virtual Student Agents (LVSAs) remains underexplored. Such agents are essential for teacher-facing applications,…
Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one…
Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower…
Low-Rank Adaptation (LoRA) enables efficient fine-tuning of pre-trained language models through low-rank matrix approximation, achieving effectiveness in many scenarios. However, its representation capacity is constrained in complex tasks…
Deploying Vision-Language Models (VLMs) on edge devices is challenged by resource constraints and performance degradation under distribution shifts. While test-time adaptation (TTA) can counteract such shifts, existing methods are too…
Large Language Models-Cognitive Assistants (LLM-CAs) can enhance Quality Management Systems (QMS) in manufacturing, fostering continuous process improvement and knowledge management. However, there is no human-centred software architecture…
Quantum computing education faces significant challenges due to its complexity and the limitations of current tools; this paper introduces a novel Intelligent Teaching Assistant for quantum computing education and details its evolutionary…
Higher education faces growing challenges in delivering personalized, scalable, and pedagogically coherent learning experiences. This study introduces a structured framework for designing an AI-powered Learning Management System (AI-LMS)…
Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system…
The development of a learning management system (LMS) as a key service seems to be very effective for creation of educational digital platforms. Such platforms for both higher education institutions and various companies can provide the…
Background: Traditional Learning Management Systems (LMS) usually offer a one-size-fits-all solution that cannot be customized to meet specific learner needs. To address this issue, adaptive learning mechanisms are integrated either by…
The widespread adoption of large language models (LLMs) marks a transformative era in technology, especially within the educational sector. This paper explores the integration of LLMs within learning management systems (LMSs) to develop an…
Large Language Models (LLMs) have advanced rapidly but face significant memory demands. While quantization has shown promise for LLMs, current methods typically require lengthy training to alleviate the performance degradation from…
A widely-used quantum programming paradigm comprises of both the data flow and control flow. Existing quantum hardware cannot well support the control flow, significantly limiting the range of quantum software executable on the hardware. By…
Quantum Approximate Optimization Algorithms (QAOA) promise efficient solutions to classically intractable combinatorial optimization problems by harnessing shallow-depth quantum circuits. Yet, their performance and scalability often hinge…