The literature shows conflicting outcomes, making it difficult to determine how e-learning affects the performance of students in higher education. The effect of e-learning was studied and data has been gathered with the utilization of a variety of qualitative and quantitative methods, especially in relation to students' academic achievements and perceptions in higher education, according to literature review that has been drawn from articles published in the past two decades (2000-2020). The development of a sense of community in the on-line environment has been identified to be one of the main difficulties in e-learning education across this whole review. In order to create an efficient online learning community, it could be claimed that both instructors and students must work together to engage in extensive collaboration and engagement with both students and one another. Since educational institutions must be ready for the sustainability regarding e-learning adoption, the presented work argues that there is a requirement for better identification and knowledge of this. The results revealed that a university's competency and capacity for meeting elearning demand stemmed from actual requirement for the implementation of e-learning for specific academic environment hinged on sustainability related to implementation of e-learning. In addition to that, each university's local culture influenced and supported the implementation process, where the inhibiting and driving variables had a substantial effect on the continuity and outcome of the process. The range of digital tools that can successfully encourage social interactions as well as the learning community need to be further researched. With regard to higher education, there is an increase in innovative assessments of variables to assess learning results in the settings of digital learning. Researchers should carefully evaluate their study designs and study subjects in digital learning environments for this reason, as well as how to handle measuring learning.
Speech enhancement aims to improve speech quality and intelligibility in noisy environments and is important in applications such as hearing aids, mobile communications and automatic speech recognition (ASR). This paper shows a structured review of speech enhancement techniques, classified depending on the channel configuration and signal processing framework. Both traditional and modern approaches are discussed, including classical signal processing methods, machine learning techniques, and recent deep learning-based models. Furthermore, common noise types, widely used speech datasets, and standard evaluation metrics for evaluating speech quality and intelligibility are reviewed. Key challenges such as non-stationary noise, data li
... Show MoreLarge language models (LLMs) are a rapidly evolving class of artificial intelligence with significant potential in clinical healthcare. Despite accelerating adoption, rigorous systematic evidence on clinical utility, patient safety, and implementation feasibility remains fragmented. To systematically review LLM applications across clinical domains, evaluate performance with appropriate contextual caveats, characterize implementation barriers, and identify ethical and regulatory considerations. Scientific databases were searched from January 2020 to January 2025. Studies evaluating transformer-based LLMs (≥10M parameters) in clinical settings were eligible. Data were independently double-extracted; quality was assessed using QUADAS-2, RE-A
... Show MoreIncreasing requests for modified and personalized pharmaceutics and medical materials makes the implementation of additive manufacturing increased rapidly in recent years. 3D printing has been involved numerous advantages in case of reduction in waste, flexibility in the design, and minimizing the high cost of intended products for bulk production of. Several of 3D printing technologies have been developed to fabricate novel solid dosage forms, including selective laser sintering, binder deposition, stereolithography, inkjet printing, extrusion-based printing, and fused deposition modeling. The selection of 3D printing techniques depends on their compatibility with the printed drug products. This review intent to provide a perspecti
... Show MoreIn recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime
... Show MoreReactive oxygen species (ROS) are produced as a result of biochemical processes that are not in balance with the body's antioxidant defense mechanism. This metabolic dysfunction is referred to the oxidative stress (OS). Metabolic dysfunction-associated diseases are affected by changes in the redox balance. It is now widely recognized that oxidative stress significantly affects diabetes mellitus (DM), particularly type 2 diabetes. The biochemical changes associated with DM could disturb the oxidative milieu, leading to several microvascular complications in diabetic patients. Thus, DM is a perfect disease to explore the harmful consequences of oxidative stress and how to treat it. Oxidative stress triggered by hyperglycemia is
... Show MoreDeveloping an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal
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