The relevance of emotions in teaching is now a widely studied aspect, although contemplation on how the teachers are able to realize, regulate and control their emotions is being deliberated upon. The article attempts to understand the concept of emotional intelligence (EI) in higher education teachers and how it can be incorporated in effective teaching as emotional intelligence competencies (EIC) for superior performance. The technique of structural equation modelling (SEM) has been applied to validate and propose a model for EI-based teaching competencies and their relation with the core competencies. It was statistically proven that EIC have a strong impact on attitude of teachers which in turns contribute highest towards superior performance. The study contributes towards research in the field of EI in teaching and suggests that institutes should give critical importance to enhancement of EIC and accordingly implement suitable training programmes for ensuring effective teaching and superior performance.

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Purpose The technological advances worldwide are posing challenges for the teaching fraternity. However, certain competencies can enable the teachers to enhance their performance by managing self and adopting flexible teaching and learning tools. The purpose of this paper is to identify, analyse and model such competencies with special reference to emotional intelligence and social media competencies (SMCs). A competency framework is developed and a subsequent performance ranking system is derived in this study. Design/methodology/approach The statistical approach of multiple regression using partial least square based strucutural equation modelling is used for model development by estimating the impact of various competencies on performance. The technique of analytical network process is applied to derive a performance management system for ranking employees. Findings The paper estimates the relative impact of various competencies on superior performance of teachers, thus enabling to develop a competency model. A performance management and ranking system has also been developed. Practical implications A working practical model for performance management and ranking of teachers is developed on the basis of different criteria having different weightage. The ranking model can enable to develop suitable strategies for making effective recruitment and appraisal decisions. Originality/value The performance management model integrates emotional intelligence competencies, SMCs along with knowledge, skills and attitude, to develop fair and weightage-based performance ranking system.

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This article aims to review the concept of team emotional intelligence (TEI) and propose a conceptual model for its enhancement. It seeks to analyze the past literature on TEI and attempts to identify and derive a relationship between different variables that influence it. The technique of interpretive structural modelling (ISM) has been used to identify the strongest and weakest drivers of TEI. The relationship between the individual- and team-level variables was established to develop a theoretical model for enhancement of TEI. The model will help organizations to focus on the right variables to enhance TEI, thus producing effective teams and efficient results. The article adds a new dimension to the approach of TEI by proposing a model for enhancing it. It also studies the different variables of TEI at individual and team levels and their interrelationships, which have not yet been explored extensively.

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In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-the-art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%.