Artificial intelligence is a technology that is altering all facets of life despite a widespread lack of familiarity. It is a versatile tool that helps individuals to reconsider how we combine information, evaluate data, and use the insights obtained to enhance decision-making. It makes sense that one of the most in-demand skills in the technology industry, which has impacted practically every industry from cybersecurity to software to IoT, has to do with artificial intelligence (AI). Business processes may be made more effective, complications can be reduced, and the quality of life can be improved thanks to the innovative applications and services that are being quickly developed using AI.

Part of Speech (POS) tagging is crucial to Natural Language Processing (NLP). It is a well-studied topic in several resource-rich languages. However, the development of computational linguistic resources is still in its infancy despite the existence of numerous languages that are historically and literary rich. Assamese, an Indian scheduled language, spoken by more than 25 million people, falls under this category. In this paper, we present a Deep Learning (DL)-based POS tagger for Assamese. The development process is divided into two stages. In the first phase, several pre-trained word embeddings are employed to train several tagging models. This allows us to evaluate the performance of the word embeddings in the POS tagging task. The top-performing model from the first phase is employed to annotate another set of new sentences. In the second phase, the model is trained further using the fresh dataset. Finally, we attain a tagging accuracy of 86.52% in F1 score. The model may serve as a baseline for further study on DL-based Assamese POS tagging.

New Delhi, Oct 27: Indian researchers have developed a new technique to assess fracture recovery. This technique, based on an artificial intelligence (AI) simulation model, could be helpful in predicting the improvement in fractures of the thigh bone after surgery. In addition, this technique can also help the surgeon choose the right implant or procedure before the surgery required for fracture healing. The researchers say this technique may be used to assess the healing outcomes of various fracture fixation strategies. This allows the optimal approach to be selected based on the patient's specific anatomical build and fracture type.