Social media data is an opportunistic resource for post-marketing surveillance of COVID-19 (Coronavirus disease of 2019) vaccines. However, the usage of the term Adverse Drug Reaction (ADR) in different contexts, which do not refer to adverse reactions to given vaccines, poses a great challenge for existing ADR extraction techniques. In the present work, a framework titled “Sequence Labelling with 2 Level Segregation” (SL2LS) has been proposed to extract adverse reactions of COVID-19 vaccines from Twitter data. It handles the mentioned challenge by deploying a Bidirectional Long Short-Term Memory (Bi-LSTM) based two-level classification model to segregate the tweets containing ADR terms that represent adverse reactions of vaccines from the tweets that comprise ADR terms which are not referring to adverse reactions of vaccines based on the context of ADR term usage and then employ domain-specific pre-trained Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) sequence labelling model for extracting vaccine linked ADRs from segregated tweets. The proposed framework has been applied to Covaxin and Covishield COVID-19 vaccine data collected from Twitter and annotated with the help of a domain expert. It achieved the highest precision, recall and F1-score of 95.46%, 93.2% and 94.33%, respectively, in comparison to other state-of-the-art techniques. The validation of detected top 20 frequent ADRs of the two vaccines with official surveillance reports resulted in 19 verified ADRs for Covaxin and all 20 verified ADRs for Covishield and one unverified ADR “Blood Clot” for Covaxin which is a valuable lead for medical experts for further exploration. Further analysis shows that the two vaccines share some common ADRs viz. “Discomfort”, “Fever”, “Pain”, “Headache” and “Fatigue” and “Discomfort” and “Fever” are the most frequent ADRs associated with Covaxin and Covishield vaccines, respectively.

In response to the global COronaVIrus Disease of 2019 (COVID-19) pandemic, widespread vaccination campaigns were initiated worldwide. To minimise Adverse Drug Reactions (ADRs) linked to the vaccines, extensive pre-clinical trials and post-marketing surveillance activities were undertaken in a traditional setting. However, there has been a limited exploration of social media data for this purpose. As social media data is composed of real-time user experiences on vaccines, its analysis is pivotal for understanding vaccine safety. This work proposes a framework named Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis (DARCVAA). It employs four Deep Neural Networks (DNN) based classification models to detect ADRs of COVID-19 vaccines from Reddit’s posts. Further, it uses the Apriori algorithm to extract associations between vaccines and ADRs to comprehensively understand their relationships. The statistical significance of the extracted associations has been evaluated in terms of support and confidence. The proposed framework has been applied to a dataset collected from Reddit’s platform from September 2020 to July 2021 and annotated with the help of a medical expert. The experimental results showed that the proposed framework has outperformed six state-of-the-art detection models, which include Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT), Vaccine Adverse Events-Mine (VAEM-Mine), Vaccine Adverse Events (VAE), Support Vector Machines (SVM) and Naıve Bayes (NB) in respect to precision, recall, F1-score and accuracy. The identified ADRs have been validated from the official surveillance reports, and the validation results have proven the efficacy of the proposed framework toward ADR detection and capturing possibly emerging ADRs. The analysis of detected ADRs in terms of frequent ADRs, ADRs unique to vaccines and genders and associations of vaccines and ADRs has led to promising insights into the safety profile of COVID-19 vaccines.

The application of Machine Learning (ML) algorithms in chemical sciences, particularly computational chemistry, is a vastly emerging area of modern research. While many applications of ML techniques have already been in place to use ML based potential energies in various dynamical simulation studies, specific applications are also being successfully tested. In this work, the ML algorithms are tested to calculate the unimolecular dissociation time of benzene-hexachlorobenzene, benzene-trichlorobenzene, and benzene-monochlorobenzene complexes. Three ML algorithms, namely, Decision-Tree-Regression (DTR), Multi-Layer Perceptron, and Support Vector Regression are considered. The algorithms are trained with simulated dissociation times as functions (attributes) of complexes’ intramolecular and intermolecular vibrational energies. The simulation data are used for an excitation temperature of 1500 K. Considering that the converged result is obtained with 1500 trajectories, an ML algorithm trained with 700 simulation points provides the same dissociation rate constant within statistical uncertainty as obtained from the converged 1500 trajectory result. The DTR algorithm is also used to predict 1000 K simulation results using 1500 K simulation data.

The activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects. But these techniques perform poorly and miss out several interesting associations when it comes to analysis of multidimensional data which may include multiple patient attributes, drugs and adverse drug reactions. In the present work, a clustering-based hybrid approach has been presented for finding quantitative multidimensional association from the large amount of data. Firstly, it employs clustering technique for segmentation of data into semantically coherent clusters. Furthermore, disproportionality method called proportional reporting ratio is applied on clustered data for generating statistically strong associations. The performance of the proposed methodology has been examined on the data taken from the U.S. Food and Drug Administration Adverse Event Reporting System database corresponding to Aspirin and nine other drugs which are prescribed along with Aspirin. The experimental results show that the proposed approach discovered a number of association rules which are very comprehensive and informative regarding relationship of patient traits and drugs with adverse drug reactions. On comparing experimental results with LPMiner, it is observed that the quantitative association rules discovered by LPMiner are just 8.3% of what have been discovered by the proposed methodology.

In this article, a facial expression recognition technique has been proposed. In this method, initially, a face image is segmented into different sub-images like left eye, right eye, mouth, nose, etc. Then, two-dimensional principal component analysis (2DPCA)-based transformation is performed on each sub-image separately to extract the features. Lastly, classification operation is made to categorize the expression of faces. To demonstrate the effectiveness of the proposed method, results are compared with other existing PCA-and 2DPCA-based methods in terms of overall classification accuracy. Results for the proposed method are found to be encouraging.