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Hearing-impaired people use sign language to express their thoughts and emotions and reinforce information delivered in daily conversations. Though they make a significant percentage of any population, the majority of people can't interact with them due to limited or no knowledge of sign languages. Sign language recognition aims to detect the significant motions of the human body, especially hands, analyze them and understand them. Such systems may become life-saving when hearing-challenged people are in desperate situations like heart attacks, accidents, etc. In the present study, deep learning-based hand gesture recognition models are developed to accurately predict the emergency signs of Indian Sign Language (ISL). The dataset used contains the videos for eight different emergency situations. Several frames were extracted from the videos and are fed to three different models. Two models are designed for classification, while one is an object detection model, applied after annotating the frames. The first model consists of a three-dimensional convolutional neural network (3D CNN), while the second comprises of a pre-trained VGG-16 and a recurrent neural network with a long short-term memory (RNN-LSTM) scheme. The last model is based on YOLO (You Only Look Once) v5, an advanced object detection algorithm. The prediction accuracies of the classification models were 82% and 98%, respectively. YOLO based model outperformed the rest and achieved an impressive mean average precision of 99.6%.

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Food adulteration is a growing peril for consumers, traders, and manufacturers worldwide. Food fraud costs the economy a fortune and creates mistrust among consumers and merchants. Black Pepper is a valuable and heavily adulterated spice. This study explores the ability of deep learning coupled with image processing to identify black pepper contaminated with its common adulterant papaya seeds. Prevalent methods work on a relatively small sample, requiring specialization and resources. Our research proposes a system to quickly and economically obtain a method to trace added impurities. It is a problematic venture for one‐shot image classification systems to differentiate between identical entities. Therefore, we implement a Siamese network that is proficient in it. A data set of 2,000 images of each class is created. After experimentation, the training and validation accuracy of 0.96 and 0.92 is obtained, while human eye judgment of the same images gave a correctness score of 0.97. Novelty impact statement A novel non‐destructive, agile, cost‐effective, user‐friendly technique to trace papaya seeds' adulteration in black peppercorns using Artificial Intelligence. The proposed system is better in accuracy than the majority of existing approaches.