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2,117 papers
The findings suggest the feasibility of Federated Learning for developing privacy-preserving and highly generalizable OMR systems for large-scale educational assessment.
The rapid growth of solar photovoltaic systems has exposed persistent challenges in their operation reliability, performance forecasting and integration with power systems. Therefore, accurate assessment of solar photovoltaic system behaviour and on-field reliability are required through comprehensive modelling to en...
FedMediFormer-XAI is introduced, a unified and proper framework that incorporates federated learning, multimodal transformers, diffusion-based data augmentation, Graph Neural Networks for drug recommendation, and Explainable Artificial Intelligence (XAI) for diabetes intelligence.
Overall, the proposed framework addresses three critical research gaps: preserving data privacy without centralized data aggregation, handling non-IID data distributions in IoT networks, and enabling efficient computation for resource-limited devices.
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A comprehensive overview of the methodologies, technologies, and pedagogical foundations underpinning AI-based personalized learning systems is presented, which investigates cutting-edge AI techniques, including deep learning, reinforcement learning, multimodal learning, and explainable AI, and illustrates their integr...
Overall, FedMVC represents a promising framework for privacy-preserving unsupervised learning in distributed environments and key research challenges include formal privacy–utility trade-offs, convergence under extreme data heterogeneity, and the integration of large language models for improved interpretability.
Viral hepatitis remains one of the most significant global public health challenges, affecting more than 300 million individuals worldwide and causing approximately 1.3 million deaths annually due to cirrhosis, liver failure, and hepatocellular carcinoma (HCC). Despite remarkable advances in vaccination strategies, hig...
Despite major medical breakthroughs, Hepatitis B (HBV) and Hepatitis C (HCV) continue to silent-kill millions globally, primarily because they go undetected until severe liver damage sets in. While direct-acting antivirals and suppressive therapies work wonders, our screening methods are lagging behind. We still rely o...
Privacy-protecting digital twins are also crucial for fields like healthcare, finance, and smart cities, where sharing data in real time makes it harder to stay safe and follow the rules. This study discovered that digital twin ecosystems and innovative methodologies, including AI-driven adaptive privacy models, federa...
This repository contains the runnable implementation and experiment scripts accompanying the paper "Detection-Aware Content-Level Differential Privacy for Lattice-Trapdoor Traceable Federated Prototype Learning". It implements a lattice-trapdoor identity-tracing mechanism for federated prototype learning (FedLHT) combi...
This chapter provides a methodical basis of how predictive analytics works in theory and practice around the field related to consumer electronics. It also commences with an overview of the fundamental concepts, namely, data mining, machine learning, and statistical modeling, and the way they contribute to the success...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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