Protecting Children from Harmful Audio Content: Automated Profanity Detection From English Audio in Songs and Social-Media

A novel approach for the automated detection of profanity in English audio songs using machine learning techniques. One of the primary drawbacks of existing systems is only confined to textual data. The proposed method utilizes a combination of feature extraction techniques and machine learning algorithms to identify profanity in audio songs. Specifically, the approach employs the popular feature extraction techniques of Term frequency–inverse document frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT) and Doc2vec to extract relevant features from the audio songs. TF-IDF is used to capture the frequency and importance of each word in the song, while BERT is utilized to extract contextualized representations of words that can capture more nuanced meanings. To capture the semantic meaning of words in audio songs, also explored the use of the Doc2Vec model, which is a neural network-based approach that can extract relevant features from the audio songs. The study utilizes Open Whisper, an open-source machine learning library, to develop and implement the approach. A dataset of English audio songs was used to evaluate the performance of the proposed method. The results showed that both the TF-IDF and BERT models outperformed the Doc2Vec model in terms of accuracy in identifying profanity in English audio songs. The proposed approach has potential applications in identifying profanity in various forms of audio content, including songs, audio clips, social media, reels, and shorts

New spectrophotometric techniques for the estimation of Perphenazine in bulk drug form

Perphenazine is an atypical antipsychotic drug.  The simple and accurate and precise absorption ratio method has been developed for the simultaneous estimation of Perphenazine in the pure drug form. The absorption maxima were found to be 310nm in Method A (0.1N HCl Buffer) and show linearity over the concentration range of 0.002-0.02 µg/mL with regression equation y=0.5372x-0.0099(r2 = 0.9990). In Method B (Sodium acetate buffer, pH 4.5) the drug obeys Beer Lambert’s law (λmax 310nm) in the concentration range of 0.002-0.02 µg/mL with regression equation y=0.4257x - 0.0084(r2= 0.9992). In Method C (Phosphate buffer, pH 6.8) the drug obeys Beer Lambert’s law (λmax 310nm) in the concentration range of 0.002-0.02 µg/mL with regression equation y=0.482x - 0.0074(r2= 0.9991). In Method D (phosphate buffer, pH 7.2) the drug obeys Beer Lambert’s law (λmax 310nm) in the concentration range of 0.002-0.02 µg/mL with regression equation y=0.3686x - 0.0055(r2= 0.9992). In Method E (0.1N NaOH Buffer) and shows linearity over the concentration range of 0.002-0.02 µg/mL with regression equation y=0.4864x-0.0081(r2 = 0.999). In Method F (Methanol) the drug obeys Beer Lambert’s law (λmax 300nm) in the concentration range of 0.002-0.02 µg/mL with regression equation y=0.6323x - 0.003(r2= 0.999). In Method G (Ethanol ) the drug obeys Beer Lambert’s law (λmax 300nm) in the concentration range of 0.002-0.02 µg/mL with regression equation y=0.3686x - 0.0055(r2= 0.9991).   First derivative spectrophotometric methods (A1, B1, C1, D1, E1, F1 and G1) were developed in 0.1NHCl and Sodium acetate pH 4.5 and phosphate buffer, in pH 6.8 and phosphate buffer, pH 7.2, 0.1N NaOH Buffer, which Perphenazine obeys Beer Lambert’s law(λmax310nm) in the concentration range of 0.002-0.02 µg/mL and  0.002-0.02 µg/mL and    0.002-0.02 µg/mL  and 0.002-0.02 µg/mL and0.002-0.02 µg/mL  with regression equations y=0.0357x - 0.0006 and y=0.0201x+0.0004 and y=0.0196x-0.0002 and y=0.0162x+0.0002  and y=0.0239x - 0.0002  and Perphenazine obeys Beer Lambert’s law(λmax300nm) for methanol and ethanol in the concentration range of 0.002-0.02 µg/mL and  0.002-0.02 µg/mL  with regression equations y=0.0423x-0.0003 and y=0.0371x+0.0003 respectively.&nbsp

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Abstracts of National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology

This book contains the abstracts of the papers presented at the National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology (NCB4EBT-2021) Organized by the Department of Biotechnology, National Institute of Technology Warangal, India held on 29–30 January 2021. This conference is the first of its kind organized by NIT-W which covered an array of interesting topics in biotechnology. This makes it a bit special as it brings together researchers from different disciplines of biotechnology, which in turn will also open new research and cooperation fields for them. Conference Title: National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental BiotechnologyConference Acronym: NCB4EBT-2021Conference Date: 29–30 January 2021Conference Location: Online (Virtual Mode)Conference Organizer: Department of Biotechnology, National Institute of Technology Warangal, Indi