Suresh Sundaram – författare

School bullying and cyberbullying are widely recognized as an international problem, but publications have focused on the western tradition of research. In India, recognition of these issues and research on the topics have been emerging in recent years. Beginning with cross-cultural differences across Indian, European and Australian contexts, this volume provides direct empirical comparisons between western and Indian situations. It then discusses innovative ways of hearing the views of students, pre-service teachers and teachers, featuring a range of qualitative and quantitative methodologies. The concluding commentaries from North American investigators provide a further international perspective from another region where much progress in researching these areas has been made. Together this ground-breaking collection comprises contributions from four continents on the prevalent issues of bullying, cyberbullying and student well-being.

School bullying and cyberbullying are widely recognized as an international problem, but publications have focused on the western tradition of research. In India, recognition of these issues and research on the topics have been emerging in recent years. Beginning with cross-cultural differences across Indian, European and Australian contexts, this volume provides direct empirical comparisons between western and Indian situations. It then discusses innovative ways of hearing the views of students, pre-service teachers and teachers, featuring a range of qualitative and quantitative methodologies. The concluding commentaries from North American investigators provide a further international perspective from another region where much progress in researching these areas has been made. Together this ground-breaking collection comprises contributions from four continents on the prevalent issues of bullying, cyberbullying and student well-being.

Over the past few decades, the field of machine learning has made remarkable strides, surpassing human performance in tasks like voice and object recognition, as well as mastering various complex games. Despite these accomplishments, a critical challenge remains: the absence of general intelligence. Achieving artificial general intelligence (AGI) requires the development of learning agents that can continually adapt and learn throughout their existence, a concept known as lifelong learning.In contrast to machines, humans possess an extraordinary capacity for continuous learning throughout their lives. Drawing inspiration from human learning, there is immense potential to enable artificial learning agents to learn and adapt continuously. Recent advancements in continual learning research have opened up new avenues to pursue this objective.This book is a comprehensive compilation of diverse methods for continual learning, crafted by leading researchers in the field, along with their practical applications. These methods encompass various approaches, such as adapting existing paradigms like zero-shot learning and Bayesian learning, leveraging the flexibility of network architectures, and employing replay mechanisms to enable learning from streaming data without catastrophic forgetting of previously acquired knowledge.This book is tailored for researchers, practitioners, and PhD scholars working in the realm of Artificial Intelligence (AI). It particularly targets those envisioning the implementation of AI solutions in dynamic environments where data continually shifts, leading to challenges in maintaining model performance for streaming data.