Artificial Intelligence

Specific emitter identification (SEI)  is a promising authentication paradigm in physical layer security (PLS). Despite the significant success of existing SEI schemes, most of them assume that the distributions of the training dataset and the test dataset are consistent. However, in most practical scenarios, when the signal parameters change, the distribution of the samples will changes,  resulting in a significant performance degradation.

ommon approaches to stunting prediction, including statistical analysis and machine learning, have poor performance due to shifts in the factors influencing stunting. Causes data cannot be integrated directly when using statistical analysis. At the same time, machine learning causes a decrease in predictive performance down over time. This study proposes a new approach to stunting prediction in infants and toddlers aged 0-5 years using continuous learning methods.

Most plant diseases have observable symptoms, and the widely used approach to detect plant leaf disease is by visually examining the affected plant leaves. A model which might carry out the feature extraction without any errors will process the classification task successfully.  The technology currently faces certain limitations such as a large parameter count, slow detection speed, and inadequate performance in detecting small dense spots. These factors restrict the practical applications of the technology in the field of agriculture.

Sentiment analysis, which aims to identify the positive or negative tone of a given text, has seen a surge in interest over the past two decades, making it one of the most studied areas of study in the fields of Natural Language Processing and Information Extraction. Due to the ambiguous nature of sarcasm, however, sarcasm detection is an essential part of sentiment analysis. The task becomes exceedingly challenging when applied to a language with a more intricate morphology and a lack of available resources, such as Telugu.