A famous psychologist or researcher, Daniel Goleman, gave a theory on the importance of Emotional Intelligence for the success of an individual's life. Daniel Goleman quoted in the research that "The contribution of an individual's Intelligence Quotient (IQ) is only 20% for their success, the remaining 80% is due to Emotional Intelligence (EQ)". However, in the absence of a reliable technique for EQ evaluation, this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms. This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools. The proposed cross intelligence evaluation uses two different aspects which are similar, i.e., EQ and SQ to estimate EQ by using a trained model over SQ Dataset. This presented analysis ensures the resemblance between the Emotional and Social Intelligence of an Individual. The research authenticates the results over standard statistical tools and is practically inspected by deep learning tools. Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) and Social IQ dataset are deployed over a Multi-layered Long-Short Term Memory (M-LSTM) based deep learning model for accessing the resemblance between EQ and SQ. The M-LSTM based trained deep learning model registered, the high positive resemblance between Emotional and Social Intelligence and concluded that the resemblance factor between these two is more than 99.84%. This much resemblance allows future researchers to calculate human emotional intelligence with the help of social intelligence. This flexibility also allows the use of Big Data available on social networks, to calculate the emotional intelligence of an individual.
INTRODUCTIONLiterature suggests that listening to music decreases the time required for a surgeon to perform surgical skills, and improves their overall performance. However, little is known about how the surgeon's preference or response to that particular music affects the rate at which they learn a novel skill. The primary goal of this study is to explore how different auditory conditions impact the rate of acquisition of an introductory surgical skill, the intracorporeal knot.METHODSParticipants will be naïve to surgical skills (medical students), and prescreened for demographics, history of musical experiences, and their use of music as a study aid. A cognitive pre‐test will be employed to assess baseline participant performance with, and without the presence of music. This will serve to stratify individuals into two groups: those whose performance thrives, or declines, in the presence of music. An equal proportion of individuals from each group will be randomly assigned to one of four auditory training conditions: participant‐preferred music, randomized music, ambient noise, and silence. Participants will watch an instructional video on the technique of the intracorporeal knot and will have 80 minutes to practice the task to proficiency. Proficiency is defined in the literature by the Fundamentals of Laparoscopic Surgery as a maximum of 112 seconds with no knots occurring outside of 1mm. Learning curves will be analyzed to determine the rate of acquisition for each group.ANTICIPATED RESULTSWe anticipate that participants will master the intracorporeal knot tying task fastest under the preferred music condition, and slowest in the randomized music condition.CONCLUSIONWhen the music is reflective of the surgeon's personal taste, it could be beneficial for the acquisition of new surgical skills. However, non‐preferred music could negatively impact skill acquisition and would suggest that minimizing of the use of music in operating rooms when residents are present would generate a more efficient learning environment.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
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