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Daily Archives: July 24, 2025

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Folding Algorithms Of Life: Mathematical Insights Into Protein Misfolding, Disorders, And Therapies

Authors: Ms. Meenal Maan, Er. Rajdeep Saharawat, Muskan,, Dr. Vinit Kumar Sharma

Abstract: Proteins must fold into specific three-dimensional structures to function correctly. Errors in protein folding—misfolding—can lead to aggregation and are associated with several degenerative diseases, including Alzheimer’s, Parkinson’s, and Huntington’s. This review explores the molecular mechanisms of protein folding and misfolding, the cellular quality control systems managing these processes, and the pathogenesis of misfolding-related disorders. We also discuss therapeutic approaches aimed at correcting misfolding or enhancing proteostasis.

DOI: https://doi.org/10.5281/zenodo.16407753

 

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A Comprehensive Observation of Video Quality Enhancement Using Machine Learning Algorithm

Authors: Kalpana Chaurasia, Sachin chourasia

 

Abstract: Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). One option to enhance the video quality is to change camera lens, which is costly. Thus, an alternative is required. Hence we also proposed convolutional neural network (CNN) may give priority to distinct aspects in an video and differentiate between them. A clear review of the literature on a few CNN-based video enhancing techniques is carried out.

DOI: https://doi.org/10.5281/zenodo.16406507

 

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