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Natural Language Processing (NLP) uses Sentiment Analysis (SA) to determine text sentiment. SA is often used on text datasets to assess consumer demands, the sentiment of the customer for a product, and brand monitoring. Deep Learning (DL) is a subset of Machine Learning (ML) that mimics how humans learn. In this work, the Deep Learning Reptile Search Algorithm (SA-DLRSA) model is introduced for accurate automatic SA. The SA-DLRSA model utilizes Word2Vec word embedding to reduce language processing that is dependent on data pre-processing. The SA-DLRSA model utilizes SVM, CNN, RNN, BiLSTM, and BERT models for sentiment classification. Choosing the optimal hyperparameters is crucial for determining the model's architecture, functionality, performance, and accuracy. The Reptile Search Algorithm (RSA) is employed to find the best optimal hyperparameters to improve classification. A derived balanced dataset based on the tweets related to bitcoins was employed as a training dataset, which contains three sentiments, namely "neutral", "positive", and negative". The collection has 7 columns and 50058 rows, consisting of 21938 neutral, 22937 positive, and 5183 negative tweets. Precision, accuracy, recall, and F1 Score metrics were used to evaluate the effectiveness of the proposed approach. The results showed that the BERT and BiLSTM classifiers achieved superior performance in classifying sentiments in the tweets achieving accuracies of 99% and 98%, respectively. Due to the promising results of the proposed approach, it is anticipated to be used in solutions to social media problems, such as hate speech detection and emotion detection. [ABSTRACT FROM AUTHOR]

Natural tubules Halloysite are unique and versatile material formed by surface weathering of aluminosilicate minerals and comprises of different proportion of aluminum, silicon, hydrogen, and oxygen. It has a chemical formula of Al4Si4O10 (OH)8.4H2O. Nano tubular geometry of halloysite exhibit nanoscale dimensions. Basically, this tubular arrangement varies with different regions. HNTs have high mechanical strength and modulus and these features make it an ideal material for preparing different polymer based composites. Halloysite Nanotubes (HNTs) are being used for so many varieties of biological and non-biological applications; remediation of environmental contaminants, act as a cargo for the delivery of drugs and various macromolecules, storage of molecular hydrogen and for catalytic conversion and processing of hydrocarbons. With this regard, the paper shows the layer by layer self-assembly approach by using polyelectrolytes (Polyallylamine hydrochloride and polystyrene sulphonate). The prolong release of saccharomyces cerevisiae for biotechnological point of view for the production of ethanol from layer by layer self-assembly was noted. The polyelectrolyte materialsshow the pH dependent release of saccharomyces cerevisiae. The pH dependent release was observed at pH 4.5, 7.0 and 10.5 which was represented by UV-Visible spectrophotometry. [ABSTRACT FROM AUTHOR]