Word Sense Disambiguation (WSD) is considered as one of the pivotal problems of Semantic classification among polysemous words that can be addressed using Natural Language Processing (NLP) for identifying the sense of the ambiguous word in a particular context. The application areas of WSD pertain to machine translation, information extraction and retrieval (IE-IR), dialogue systems, and automatic summarization kind of NLP solutions. This paper presents a survey on WSD approaches in major AI-NLP methods by comparing different approaches for WSD in supervised, unsupervised, and knowledge based algorithms. This paper also aims at providing gap analysis in surveyed WSD systems comparing strengths and weaknesses of various surveyed systems and their accuracy. Based on the findings, a future hybrid approach synergizing rule-based and machine learning based methods are contemplated. The findings of this survey are envisaged through an ongoing research on WSD based Meta-Search algorithm under C-DAC purview for an Intelligent NLP based system to detect the actual sense of search queries and providing semantic classification of news headlines and snippets containing ambiguous words.

One of the global issues that matters most right now is healthcare. The primary cause of death globally is brain stroke. A valuable contribution to medicine is the early prediction and identification of brain stroke events. Numerous factors, including BMI (body mass index), age, sex, family history, gender, smoking status, hypertension, and so on, are linked to brain stroke deaths. While forecasting heart illness has received a great deal of interest in the medical community, predicting a brain stroke has received less attention. This study's primary goal is to evaluate various previously published research publications and select the most effective machine learning methods for brain stroke prediction for our next projects. It was shown that mortality rate and functional outcomes are the expected outcomes for the majority of the study work done after analyzing the various machine learning techniques used for stroke predictions and after accounting for the previously published studies. The techniques that were used most commonly were LR, DTC, RFC, SVM, KNN.