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As deep learning continues to develop at a fast pace, the field of medical imaging has also seen significant changes due to new technologies making it possible to automatically analyse large amounts of complex image data with much greater accuracy and efficiency than ever before. In this chapter, we will describe and e...
Additive Manufacturing (AM) has grown from a rapid-prototyping tool into a legitimate production technology, yet its broader industrial adoption continues to be constrained by process variability, stochastic defect formation, and the near-impossibility of mapping complex Process Structure Property (PSP) relationships t...
The convergence of blockchain and federated learning (FL) will be immensely beneficial for the Internet of Vehicles (IoV) in addressing some of the major concerns such as data security, privacy, scalability, and real-time decision-making. IoV systems architecture inherently creates extensive sensitive data, which is su...
Training code of the six compared methods (FedAvg, FedAvgM, FedProx, FedNova, SCAFFOLD and FedHAD), experiment runners, raw per-seed results and telemetry, analysis scripts that reproduce every table and figure of the paper, and the pre-registrations of the control batteries. FedHAD derives each client's number of loca...
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ABSTRACT The reliance on vocal communication services, particularly in environments where the security aspects are highly valued such as emergencies, call for reliable methods for that detect the manipulation of environmental sound. One of the current challenges is the detection of SceneFake audio where only the enviro...
In present times, the rise of electronic healthcare has made vast amounts of public health data available, including clinical health records, laboratory results, genomic datasets, and public health–monitoring data. These datasets give significant opportunities for artificial intelligence (AI) to strengthen healthcare b...
The combination of artificial intelligence (AI) and precision medicine could potentially change the way medicine is practiced. In this scenario, advances in precision medicine technology will enable healthcare providers to identify the phenotype of each patient who has a unique β-cell response to treatment, as well as...
EL-RAKHAWI BIO-COGNITIVE ENGINEERING: EXECUTIVE SUMMARY This treatise establishes the El-Rakhawi Doctrine of Comprehensive Bio-Cognitive Engineering, integrating three post-AlphaFold pillars: 1. **Causal Bio-Ontological Knowledge Graph (CBOKG)**: Merges bio-ontologies with Hidden Markov Models and stochastic grammars (...
Deep learning has been a key component of recent advancements in medical image processing, but traditional centralized learning models raise serious questions about data privacy in health data. Federated Learning (FL) is a novel paradigm for training models across institutions without sharing any raw medical data. In t...
Mobile learning scenarios in adult education impose mutually constraining technical requirements regarding real‑time interaction performance, recommendation accuracy, and data privacy protection. Neither cloud‑centric centralized inference nor device‑only local com‑ putation can adequately satisfy these demands. In thi...
[1] N. J. Sarna, F. A. Rithen, U. S. Jui, S. Belal, Al Amin, T. K. Oishee, and A. K. M. Muzahidul Islam, “AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review,” IEEE Access, vol. 13, pp. 141204–141233, 2025, doi: 10.1109/ACCESS.2025.3596060. [2] A. A. Almazroi and N. Ayub, “Online P...
The pharmaceutical industry faces persistent challenges in discovering and developing safe, effective, and affordable medicines. Artificial intelligence (AI) has emerged as a transformative computational technology capable of supporting multiple stages of the drug-development pipeline. Machine learning, deep learning,...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Mobile Systems
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