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1,728 papers
As AI adoption grows in personal data management and healthcare, privacy risks from data breaches escalate. Traditional models rely on sensitive data for training, exposing users to vulnerabilities. Blockchain offers a decentralized, immutable solution with encryption and no central authority to mitigate these issues....
Dam safety and health monitoring are critical to ensuring the long-term reliability of water infrastructure, given the complex behavior of dams and the potentially severe consequences of failure. To evaluate current monitoring approaches, this study employs a structured literature review methodology that includes syste...
Open data has become central to biomedical research and public health, but health information is uniquely sensitive and difficult to share responsibly. In this narrative review, open data is considered as a spectrum of health-data sharing arrangements, ranging from public aggregate datasets to controlled-access reposit...
Rising natural calamities demand intelligent disaster management beyond traditional reactive methods. IoT-ML integration revolutionizes this by enabling real-time environmental monitoring via sensors, drones, and satellites tracking temperature, humidity, air quality, seismic activity, and water levels. ML algorithms—d...
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The proposed framework, EcoSense-AI, combines Energy-Weighted Federated Learning, Adaptive Differential Privacy, Energy-Aware Graph Neural Network-based topology modeling, and multi-objective resource optimization to facilitate secure, efficient, and adaptive edge-cloud collaborative intelligence for sustainable WSN-Io...
The chapter tries to resolve this emerging problem of machine learning-based smart heterogeneous networks, and cybersecurity by creating a multi-layered security infrastructure which is scalable to detect and react to security attacks in real-time. It proposes a multi-modal deep learning (CNNLSTM), adaptive control by...
The combination of artificial intelligence (AI) analytics with the Internet of Things (IoT) allows smart retailers to track customers in real-time using technologies such as radio frequency identification (RFID), smart shelves, and beacons. Optimizing inventories, making purchase predictions, and providing personalized...
In Internet of Things-based smart healthcare, while medical data sharing and model training are driving industry development, sensitive patient information is easily stolen and misused, and traditional data processing models struggle to balance security and usability. This research aims to construct an intelligent priv...
Post-disaster coordination is often hindered by information fragmentation. This paper reviews AI and crowdsourcing integration to address these challenges. We examine three core areas: situational awareness via text and geospatial data, logistics coordination using predictive modelling, and ethical deployment concerns....
Artificial intelligence (AI) is increasingly transforming cancer management by enabling the analysis of complex multimodal data generated across the cancer care continuum. This structured narrative review synthesizes current applications of AI, machine learning, and deep learning in cancer detection, imaging diagnosis,...
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the substantial memory footprint required for model training imposes a critical memory wall on resource-constrained edge devices, severely limiting their participation and compromising the inc...
Background: Parallel and distributed execution is proposed to relieve throughput, trainability and resource constraints in variational quantum algorithms (VQAs), but the label “parallel” covers physically and statistically different interventions. We assessed what has actually been measured on noisy intermediate-scale...
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.
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