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The COVID-19 pandemic has been a menace to the World. According to WHO, a mortality rate of 1.99% is reported as of 28th November 2021. The need of the hour is to implement certain safety measures that may not eradicate but at least put a restriction on the rising number of COVID-19 cases all over the World. To ensure that the COVID-19 protocols are being abided by, a Convolutional Neural Network (CNN)-based framework “Co-Yudh” is being developed that comprises features like detecting face masks and social distancing, tracking the number of COVID-19 cases, and providing an online medical consultancy. The paper proposes two algorithms based on CNN for implementing the above features such as real-time face mask detection using the Transfer Learning approach in which the MobileNetV2 model is used which is trained on the Simulated Masked Face Dataset (SMFD). Further, the trained model is evaluated on the novel dataset—Mask Evaluation Dataset (MED). Additionally, the YOLOv4 model is used for detecting social distancing. It also uses web scraping for tracking the number of COVID-19 cases which updates on a daily basis. This is an easy-to-use framework that can be installed in various workplaces and can serve all the purposes to keep a check on the COVID-19 protocols in the area. Our preliminary results are quite satisfactory when tested against different environmental variables and show promising avenues for further exploration of the technique. The proposed framework is a more improved version of the existing works done so far.

Computer Science and Engineering have given us the field of automata theory, one of the largest areas that is concerned with the efficiency of an algorithm in solving a problem on a computational model. Various classes of formal languages are represented using Chomsky hierarchy. These languages are described as a set of specific strings over a given alphabet and can be described using state or transition diagrams. The state/transition diagram for regular languages is called a finite automaton which is used in compiler design for recognition of tokens. Other applications of finite automata include pattern matching, speech and text processing, CPU machine operations, etc. The construction of finite automata is a complicated and challenging process as there is no fixed mathematical approach that exists for designing Deterministic Finite Automata (DFA) and handling the validations for acceptance or rejection of strings. Consequently, it is difficult to represent the DFA’s transition table and graph. Novel learners in the field of theoretical computer science often feel difficulty in designing of DFA The present paper proposes an algorithm for designing of deterministic finite automata (DFA) for a regular language with a given prefix. The proposed method further aims to simplify the lexical analysis process of compiler design.

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