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References Buenaño-Fernández, D., Gil, D. & Luján-Mora, S. Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability 11 (10), 2833 (2019). Google Scholar  Belachew, E. B. & Gobena, F. A. Student performance prediction model using machine learning approach: the case of Wolkite university. Int. J. Adv. Res. Comput. Sci.

ComputerBase conducted a massive online blind test, allowing thousands of gamers to vote on their preferred game video output. The options included NVIDIA's latest Deep Learning Super Sampling 4.5, AMD's FSR Upscaling AI with FSR 4 on Radeon RX 9000 series GPUs, and native resolution rendering without AI upscaling. Interestingly, DLSS 4.5 received the highest

GSB Lecturer Monisha Perkash facilitates a classroom discussion with students in Ecopreneurship Foundations. Entrepreneurship today looks fundamentally different than it did thirty years ago. Technologies evolve at unprecedented speed, societal challenges increasingly intersect with markets, and the line between business, policy, and impact continues to blur. The skills required to build, lead, and invest in

With the impact of artificial intelligence (AI) on everyone’s mind, companies are seeing investors ask an increasing number of AI-focused questions in shareholder engagement meetings. These questions reflect a growing consensus among institutional investors that effective AI governance is inextricably linked to fiduciary duty, long-term financial performance, and sustainable economic growth drivers. Although the answers

SeaCast is an innovative high-resolution forecasting system for the Mediterranean that harnesses AI to deliver faster and more energy-efficient predictions than traditional models. Unlike existing global AI models, which operate at lower resolutions and primarily rely on ocean data, SeaCast integrates both ocean and atmospheric variables, capturing complex regional dynamics. Its graph-based neural network accounts