RESEARCH

Finding answers to relevant questions

Faculty at FLAME University enjoys enormous opportunities and avenues for cutting-edge research in their respective areas of specialization strengthening the vision, awareness and synergy of the inter-disciplinary approach of education.

Excellence achieved through this research, in turn, creates new learning and further enriches the diversity of knowledge by synthesizing and integrating insights from a range of disciplines. This unique academic cooperation between multiple disciplines does transcend the boundaries of disciplines, and results in an insightful and consequential research output. This process of creating synergy between excellence and diversity - a dynamic motion that advances academic learning through constant interconnectivity - is the basic driving force that refines education and research at the University.

Publications


journal
 ABDC : A | SCOPUS® Q1

journal
| SCOPUS® Q1

The outbreak of COVID-19 has spread to the entire world and is severely affecting social psychology. We conducted semi-structured interviews on 59 subjects from India to investigate the impact of information, misinfodemics (spread of wrong information), and isolation on their psychology. We perform qualitative analysis on the data. Our findings reveal that flow of information leads to anxiety, caution, and knowledge; while misinfodemics cause panic, distrust, and confusion; and isolation creates cognitive dissonance (the state of having inconsistent thoughts, beliefs, or attitudes) and adaptability among masses. The encouraging part of our findings is that, as of now, the situation is far from the state of depression. Practically, our research calls upon the government to support the masses in fighting through the crisis by focusing on pointed psychological counseling. We contribute theoretically to the body of knowledge in the field of social psychology, which is studying the psychological interventions to avoid panic amid pandemic. Future researchers in the area would do well by detailing the psychological interventions required to contain the negative impacts of the pandemic on social psychology.

chapter

journal
| SCOPUS® Q1

In this piece I argue that the pandemic with its emphasis on social distancing as a desirable civic norm can reconfigure popular understanding of mature female singlehood in India- a condition that is often described in the language of lacks and social failures. The pandemic, I argue, has reaffirmed the everyday practices of upper middle-class professional women (ages 50–60 years) lending them as positive agentic subjects who are invested in self-actualization and an appreciation of intimate solitude. Overall, by specifically focusing on subjectivities and social aspirations of my interlocutors during the pandemic, I illuminate ways in which middle aged selfhood is lived in all its fragility, ambivalence and emergent possibilities.

journal
| SCOPUS® Q1

Awaited

conference

Protein Function identification has become an important task due to a plethora of new genomes being sequenced. Recently, distributed representation [1] of words in the form of continuous vector representations has been found to be a very efficient way to represent semantic/syntactic information. In this representation, each word is embedded in an n- dimensional space with similar words having proximate vectors in the embedding space. In the popular skip-gram configuration, the current word is used by the model to predict its surrounding words. In this work we introduce reduced amino acid alphabets based, distributed representation for protein sequences. In our RA2Vec (Reduced Alphabets to Vectors) implementation we first map all Swiss-Prot sequences to hydropathy and conformational similarity based reduced form. Further, by employing skip-gram based method, reduced alphabets embedding vectors (RA2Vec) were created for each set. Embedding vectors for sequences with original ProtVec representation [2] were also created. These vectors were created for various combinations of K-grams and vector sizes. All seven combinations of the original ProtVec embedding vectors, Hydropathy based embedding vectors and Conformational Similarity based embedding vectors were then employed as input to Support Vector Machines classifiers and classification models were built. The embedding vectors were further reduced using recursive Feature Elimination (RFE) method to maximize fivefold CV accuracy. We assessed the validity and the utility of the new representations employing five different data sets. Our results with all data sets indicate, certain synergistic combinations of new representations with and without ProtVec embedding can result in significantly improved performance.

conference

journal
| SCOPUS® Q2

journal
| SCOPUS®

journal
 ABDC C | SCOPUS®