This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling approach that addresses challenges such as imbalanced datasets, categorical data handling, and model interpretation. The methodology includes careful consideration of training and testing strategies, baseline model establishment, and the development of calibrated predictive models. The research emphasizes the importance of model interpretation using techniques like SHAP values to provide actionable insights for organizations. Key design choices in algorithm selection, hyperparameter tuning, and probability calibration are discussed. This approach enables organizations to proactively identify attrition risks and develop targeted retention strategies, ultimately redu

Common problems in playing online mobile and computer games were related to toxic behavior and abusive communication among players. Based on different reports and studies, the study also discusses the impact of online hate speech and toxicity on players' in-game performance and overall well-being. This study investigates the capability of pre-trained language models to classify or detect trash talk or toxic in-game messages The study employs and evaluates the performance of pre-trained BERT and GPT language models in detecting toxicity within in-game chats. Using publicly available APIs, in-game chat data from DOTA 2 game matches were collected, processed, reviewed, and labeled as non-toxic, mild (toxicity), and toxic. The study was able to collect around two thousand in-game chats to train and test BERT (Base-uncased), BERT (Large-uncased), and GPT-3 models. Based on the three models' state-of-the-art performance, this study concludes pre-trained language models' promising potential for addressing online hate speech and in-game insulting trash talk.

This guide to'colliding opposite disciplines with your research' is intended to help students and researchers, or indeed anyone who might otherwise be looking for some ideas on how to approach research or methods for designing concepts and solutions, to broaden their thinking and approach to research. This guide is mainly focused on the disciplines of science and engineering with the idea of collaborating with other distinct disciplines. However, the overall principles remain for any multidisciplinary research. With the assistance of this guide, it will help to open new ways of thinking about research, highlight the'unseen' benefits of multidisciplinary approaches to research and how they can be extremely advantageous and can lend for an optimal delivery. It will help you to contemplate how, when, and why you should open up your research to other disciplines.

Scientists at the University of Toronto have successfully tested the use of machine learning models to guide the design of long-acting injectable drug formulations. The potential for machine learning algorithms to accelerate drug formulation could reduce the time and cost associated with drug development, making promising new medicines available faster. The study was published today in Nature Communications and is one of the first to apply machine learning techniques to the design of polymeric long-acting injectable drug formulations. The multidisciplinary research is led by Christine Allen from the University of Toronto's department of pharmaceutical sciences and Alán Aspuru-Guzik, from the departments of chemistry and computer science. Both researchers are also members of the Acceleration Consortium, a global initiative that uses artificial intelligence and automation to accelerate the discovery of materials and molecules needed for a sustainable future.

"How big is the universe?" asks Alicia Nash as her face beamed with curiosity and allure. I know because all the data indicates it's infinite," answers John Forbes Nash Jr. with confidence even though there is no evidence to support his statement. "I don't; I just believe it," he says with a rather innocent smile. Though Ron Howard's A Beautiful Mind focused loosely on Nobel prize winner John Forbes Nash's battle with schizophrenia, it did point to his unique ability to see patterns where no patterns exist. He viewed the world in a different light, and that was all he needed to make his mark in history. NICE Actimize is a software company that helps its customers in combating financial crimes.

And because AI students are trained in such a rich multidisciplinary environment, they have excellent career opportunities. To make a thinking machine is one of humanity's oldest dreams. And since Allan Turing?s 1947 lectures on AI, programmable computers seemed to be the best way to go. Expectations were extraordinarily high in the 1950s and '60s, but without major breakthroughs, the whole subject lapsed temporarily into obscurity. Now, advances in the cognitive sciences that are improving our understanding of the nature of intelligence, memory, and perception from the biological perspective, coupled with the ready availability of ever-faster computers, are creating a second spring for AI--and a mecca for multidisciplinary scientists.