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Chemical entities can be represented in different forms like chemical names, chemical formulae, and chemical structures. Because of the different classification frameworks for chemical names, the task of distinguishing proof or extraction of chemical elements with less ambiguous is considered a major test. Compound named entity recognition (NER) is the initial phase in any chemical-related data extraction strategy. The majority of the chemical NER is done utilizing dictionary-based, rule-based, and machine learning procedures. Recently, deep learning methods have evolved, and, in this chapter, the authors sketch out the various deep learning techniques applied for chemical NER. First, the authors introduced the fundamental concepts of chemical named entity recognition, the textual contents of chemical documents, and how these chemicals are represented in chemical literature. The chapter concludes with the strengths and weaknesses of the above methods and also the types of the chemical entities extracted.

The coronavirus disease 2019 (COVID-19) is an exceptionally contagious and pathogenic viral disease brought about by the SARS-CoV-2 infection that was first detailed at Wuhan, China, and later it spreads everywhere throughout the world. It was a severe acute respiratory syndrome (SARS)-like virus, and the primary host of this virus is bats, revealed through genome analysis. The intermediate medium between human and bats is still unknown, and it is quite contagious, resulting in rapid spreading of this disease. All things considered, there are no clinically endorsed medications or antibodies for this sickness, yet a few precautions and staying away from close contacts are suggested. The number of COVD-19 confirmed cases is rapidly increasing with higher mortality rates, more than other epidemics that occurred in the last century although it still fluctuates. We will analyze the COVID-19 outbreak situation considering confirmed cases, recoveries, mortality rate percentages, etc. India and Bangladesh are two geographically important countries, and we will also analyze the COVID-19 situation for these two countries either with similarities or differences.

COVID-2019 has been seen as an overall pandemic, and a couple of assessments are being driven using distinctive numerical models to envision the presumable progression of this plague. These numerical models subject to various elements and examinations are needy upon likely tendency. The huge measure of COVID-19 treatment information in overall medical clinics likewise requires propelled AI techniques for examining customized restorative impacts for assessing new patients, for example, hospitalization forecast, which can give better mind to every patient as well as add to neighbourhood emergency clinic game plan and activity. This chapter acquaints a target approach by anticipating the continuation of the COVID-19 utilizing a straightforward, however amazing technique to do as such. Accepting that the information utilized is solid and that the future will keep on following the previous example of the infection, our figures recommend a proceeding with increment in the confirmed COVID-19 cases with sizable-related vulnerability. The dangers are a long way from symmetric as thinking little of its spread like a pandemic and not doing what is needed to contain it is substantially more extreme than overspending and being over cautious when it will not be required. This chapter portrays the course of events of a live forecasting exercise with monstrous possible implications for arranging and dynamic and gives target figures to the confirmed instances of COVID-19.

Late advances in innovation have empowered individuals to add area data to informal communities’ geolocations where individuals share their correspondence and whereabouts in their day by day lives, yet in addition during anomalous circumstances, for example, emergency occasions. Be that as it may, since the volume of the information surpasses the limits of human logical capacities, it is nearly inconceivable to play out a direct subjective investigation of the information. The emerging field of visual investigation has been acquainted with tackle such difficulties by integrating the methodologies from factual information examination and human computer into exceptionally intelligent visual situations. The researcher extracts valuable hidden data from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. This paper proposes sentiment analytics of geo-tagged for anomalous social movement during COVID-19 by incorporating a novel classification technique and by a visual analytics approach for forecasting the overall flow of human crowds.

The Corona Virus disease (COVID-19) epidemic outbreak leads to worldwide lockdown. Lockdown is enabled to be secure and to keep a correct social distance. According to the Government of India, every university, college and school has been closed defending against this threatening virus. This lockdown time presents an eye-opener for the digital services, for example, use of applications, generating virtual classrooms, online mock testing, online video quizzes, live lectures, deliberations, and document sharing and so on which provides more efficient than ever before. It mostly reveals essential e- learning in education, particularly during this isolation. This paper would assist in identifying attitudes of students’ towards e-learning during COVID-19 epidemics using Ensemble Learning-based Sentiment Analysis (ELSA) Algorithm. The study conducts for students studying in different schools, universities and colleges to obtain other data on e-learning involvement during these epidemics. Evaluation metrics, for example, precision, recall, F-score and accuracy are computed and examine for classification performance.

The COVID-19 pandemic situation had imposed unexpected changes in our day-to-day life, especially in terms of how we work and how we learn. Before COVID-19, e-learning methodologies were embraced only by inquisitive learners and by few corporate companies and educational institutions to disseminate knowledge through an alternative mode of content delivery. Although e-learning offers great flexibility to the learners to learn at their own convenient time and pace, by accessing the content via any smart device irrespective of their geographical location, it has got some limitations too due to which most of the teachers/students, as well as the educational institutions, prefer the traditional method of teaching/learning over online learning. In this paper, exclusive sentiment analysis has been carried out to assess the perception of the students on the adoption of online learning using a dataset that has been collected during the pre-COVID-19 pandemic period. The results of the analysis demonstrate that online learning was not as popular and widely adopted among the student’s fraternity as it is during this COVID-19 pandemic period.

With an ever-changing lifestyle and eating habits, there comes a new type of disease and viruses. And to keep humans safe from these newfound diseases and viruses, there is a need to discover new medicines and drugs regularly. Though before any drug is released in the market, it must go over a Drug Discovery Lifecycle. In the pharmacology world, it is termed clinical trials. A typical clinical trial phase consists of four phases. Once the drug is approved by the regulatory authorities, it hits the market and doctors start prescribing it. Now the thing with clinical trials is that it is, at max, tested on some thousands of volunteers within a specific geography, gender, age group, etc. Within the pharmaceutical industry, Adverse Event reporting is a critical and time-consuming part of ensuring the safe and effective use of medicines by patients. Pharmacovigilance employees process a growing number of cases from sources as varied as patients’ social media accounts to reports from investigators overseeing clinical trials. Though adverse drug reaction (ADR) reporting is a very mature process for traditional channels like clinical trials and customer care reporting, it is largely unexplored for reporting on social networking sites, and hence they tend to be missed out, that is, not reported. With an ever-increasing volume of such safety reports, pharma companies are struggling to keep pace with managing it on a timely basis, and hence there is a huge demand to automate the ADR reporting process. This chapter aims to explore social network reporting of ADRs for a specific drug and build a framework that any pharma company can use in their pursuit of automating the social network reporting of ADRs

Data visualization is an arrangement that presents data in manners that enable the utilization of human subjective and visual capacities. It is the method used to convert crude information into some visual structure. It uses designs and visuals to help with the psychological burden of comprehending big data. The burden put on general well-being because of the coronavirus is exacerbated by the ceaseless rise of new strains and unanswered inquiries concerning viral spread inside the host. Observations regarding the course of coronaviruses in people have been step by step expanded and extended to numerous regions worldwide. These observation programs have created an enormous amount of genomic information regarding coronaviruses, which encourages the investigation of the infection by computational strategies that are proficient and cost effective.