Recent advancements in technology have seen a significant reduction in human intervention. New technologies are introduced to replace outdated conventions where emphasis is put on the incorporation of Artificial Intelligence and Automation in our daily lives. With the current COVID-19 pandemic, reducing human interaction in everyday acti ons has now been deemed necessary. One of the most important sectors affected, the shopping industry, needs to adapt to meet government and social security standards. This paper addresses a S mart S hopping Cart S ystem where the entire shopping experience is automated and handled by the customer. It details a more efficient online mode of shopping which not only reduces the need for hands-on staff but also provides specialized recommendations to users using collaborative clustering to update the shopping experience and meet the demands of our time. The current shopping system has many limitations, and introduction of Radio-frequency identification (RFID) technology as the core identification mechanism can prove useful for applications such as security, safety and inventory management.

The COVID 19 pandemic is a humanitarian emergency that poses an enormous threat to society and has impacted various social media platforms and journalism. News and social media has become an immensely popular platform for consumption of information. However, these platforms are also the bearer of fake news and information which causes negative effects and creates panic. Thus, this research work aim to tackle this problem by creating a unique hybrid model using Machine learning algorithms with Natural Language Processing (NLP) techniques to verify news. In order to make the proposed system foolproof, a superior content based recommendation system is developed which will encourage users to consume authenticated news and content from verified sources. Thus, such a system will provide a holistic approach as it not only verifies but also provides genuine and true recommendations for the same.

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Mental health plays an integral part in leading a healthy life and having a positive outlook. This impacts our behavior, thought process, and actions and therefore it’simportant to identify and detect mental disorders in an early stage as it’s effects can have a lasting influence on one’s life. According to WHO, one in four people get affected by mental health disorders and currently 450 million people suffer from such conditions. Natural Language Processing can be a useful tool to analyze the trends in therapy transcripts. They can be further trained and optimized to derive useful insights and predict plausible future trends. Our proposed system analyses therapy transcripts and classifies it as ’Early signs of depression’ and ’Serious after-effects of prolonged depression’ based on the nature of the responses. Our system uses three different classifiers- Naïve Bayes, Support Vector Machine, and Logistic regression as well as two different victories- TF-IDF and Count, to classify the text into these categories. This proposed system will not only help patients in identifying their symptoms but will also help therapists and researchers in gathering a large amount of data which could be used in predictive analysis, diagnosis and understanding the patient. Such research will pave the way for improving counselling and therapy sessions and be a very essential analysis tool for therapists