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This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

The ability to exploit students' sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students' data using machine learning techniques, and (ii) modelling and predicting students' emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders. [ABSTRACT FROM AUTHOR]

The 'smartphone addiction' is a popular theme in media. It has number of clear behavioural changes in addicts' life and some of these aspects are yet to get due research attention. The present study identifies antecedents of smartphone addiction and its associated conflicts. The findings are based on data collected from adolescents, who are major targets for smartphone advertising and also vulnerable to addictions. The findings established 'loneliness' and 'self-regulation' as the main antecedents for smartphone addiction along with family, personal conflicts and poor academic performance as the significant negative consequences of its excessive use. The study findings would help to create awareness and offer insights for developing effective interventions for addressing smartphone addiction amongst adolescents. The planners, regulatory and administrative authorities will use the study findings to formulate measures that would promote positive coping mechanism to prevent smartphone addiction among adolescents. [ABSTRACT FROM AUTHOR]

Over the last few years there has been an increased emphasis on conceptualising, developing, and implementing virtual Learning initiatives across the Indian education sector. Effective and successful use of Virtual Learning Environments (VLEs) in academic setting requires identifying and solving many important social and behavioural issues. Without identifying and addressing these issues, their use can compound the mistakes of the past and leave the learner with a passive, un-engaging experience, leading to incomplete learning and low performance. Therefore, educators are required to recognise learner’s attitude, readiness, and learning style to take advantage of VLE. This study was conducted to find interrelationship of student attitude, readiness, and learning style towards the effective use of VLE. A total of 240 students who are using any form of VLE for their learning and research volunteered for the research, and the scales were filled via the convenience sampling method. The scales of learning readiness, attitude, and learning style were administered to the participants. The relationship among the variables of the research and the research hypotheses for the model were tested using different statistical tools like correlation,t-test and regression. The research result revealed that there is a correlation among learning readiness, attitude, and learning style, and each of these factors influence the effective use of VLE. [ABSTRACT FROM PUBLISHER]