Study on the effect of different contact times on the migration of heavy metals into different foodstuffs served in plastic cups

Heavy metal concentrations of Pb, Cd, Cr, and Cu leaching from single-use plastic cups were identified, and the risks associated with them were assessed in real time (up to 10 min). All samples (tea, carbonated beverage, and lassi) were placed in disposable plastic cups and held for less than 1 min, 5 min, and 10 min, respectively. Prior to digestion, the solids were ashed in a muffle furnace at 550 °C for 30 min. The filtrates were then tested for heavy metals. The samples were all confirmed to be contaminated with heavy metals. Heavy metals leached from the plastic cups in the following order: Cu > Pb > Cr > Cd. The samples' HI values were less than one, hence there was no evidence of a non-carcinogenic risk. The ILCR values for this heavy metal contamination in samples exceed 10−3, indicating a high carcinogenic risk. Lassi poses the highest possible carcinogenic risk. A rise in temperature and a drop in pH also resulted in heavy metal migration. Heavy metal leaching from plastic cups poses a serious health risk due to its toxicity. The proposed legislation should prohibit or restrict the serving of warm and hot beverages in plastic cups

Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis

This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model

Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep learning methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest and SVM. Results show deep learning models, particularly ResNet50, outperform traditional ones, with an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure and expertise is crucial for successful deep learning integration, offering a competitive edge in banking decision-making

Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs

This research examines the potential of Convolutional Neural Networks (CNNs), including VGG16, ResNet50, and InceptionV3, in predicting ecommerce profits. Emphasizing the importance of high-quality datasets, the study showcases the superior performance of CNN models over traditional algorithms, particularly noting a notable accuracy rate of 92.55% with CNN (VGG16). These results highlight deep learning's capability to extract actionable insights from complex ecommerce data, offering significant opportunities for revenue optimization and operational efficiency improvement. The conclusion underscores the need for investment in infrastructure and expertise for successful CNN integration, alongside ethical and privacy considerations. This research contributes valuable insights to the discourse on deep learning in ecommerce, offering guidance to businesses navigating the competitive global market landscape