Chimaphila umbellata; a biotechnological perspective on the coming-of-age prince's pine.

Chimaphila umbellata has been studied for almost two centuries now, with the first paper exploring the phytochemistry of the plant published in 1860. Almost all contemporary studies focus on the biotechnological advances of C. umbellata including its utilization as a natural alternative in the cosmetic, food, biofuel, and healthcare industry, with a special focus on its therapeutic uses. This literature review critically investigates the significance and applications of secondary metabolites extracted from the plant and presses on the biotechnological approaches to improve its utilization. C. umbellata is home to many industrially and medicinally important phytochemicals, the majority of which belong to phenolics, sterols, and triterpenoids. Other important compounds include 5-hydroxymethylfurfural, isohomoarbutin, and methyl salicylate (the only essential oil of the plant). Chimaphilin is the characteristic phytochemical of the plant. This review focuses on the phytochemistry of C. umbellata and digs into their chemical structures and attributes. It further discusses the challenges of working with C. umbellata including its alarming conservation status, problems with in-vitro cultivation, and research and development issues. This review concludes with recommendations based on biotechnology, bioinformatics, and their crucial interface.

Customer churn prediction using composite deep learning technique.

Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company's services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract features without taking into account the sequence information. In view of these issues, the current study provides an effective method for predicting customer churn based on a hybrid deep learning model termed BiLSTM-CNN. The goal is to effectively estimate customer churn using benchmark data and increase the churn prediction process's accuracy. The experimental results show that when trained, tested, and validated on the benchmark dataset, the proposed BiLSTM-CNN model attained a remarkable accuracy of 81%.