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2,057 papers
Artificial intelligence (AI) is increasingly being applied across the energy sector to support forecasting, diagnosis, optimization, control, and system planning. This review synthesizes common applications of AI in electricity-centered energy systems, including load and renewable-energy forecasting, electricity-market...
Code, prespecified analysis protocol, leakage unit tests and run-level results accompanying the article 'Federated Semi-Supervised Graph Neural Networks for the Prediction of Gestational and Other Diabetes from Tabular Records: A Leakage-Controlled Evaluation'. No patient data are included; the datasets are available f...
Autonomous AI-Based Cloud Security Monitoring and Attack Prediction System Using Deep Neural Networks presents a comprehensive approach to modern cloud cybersecurity by combining cloud monitoring, artificial intelligence, deep learning, anomaly detection, attack classification, threat prediction, risk assessment, and a...
Federated learning reduces the need to centralize raw data, but does not prevent privacy leakage from client updates, aggregation messages, or the final released model. This work compares local differential privacy (LDP), central differential privacy (CDP), record-level differentially private stochastic gradient descen...
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This repository contains the reproducibility materials for the FedCRM-DP study, a privacy-preserving cross-silo federated learning framework combining federated optimization, per-client record-level differential privacy, and SecAgg+ secure aggregation. The package contains source code, frozen experimental configuration...
This study evaluates federated continual learning for heart failure risk prediction under changing clinical data conditions. Using a simulated 12-month, five-site dataset, it compares attention-based continual learning with FedAvg, FedProx, linear, and replay-based approaches. The findings show that apparent benefits o...
Disease diagnosis using medical images has become an indispensable part of healthcare today. This necessitates the development of smart and effective models. This research work proposes a MediFusionNet, a hybrid web-based CNN diagnostic tool for multi-disease diagnosis based on feature fusion between VGG16 and MobileNe...
This paper compares the reasoning of Chinese and American courts in copyright disputes over training data, traces the concentration of infringement risk at the data-input stage, and considers why the fair use doctrine fits these cases poorly and why licensing and compensation mechanisms no longer work.
*** PREPRINT / AUTHOR-ACCEPTED VERSION ***This paper was presented at the conference and is the author-accepted camera-ready version. It is posted here for self-archiving purposes in accordance with the IEEE Author Posting Policy prior to official publication and indexing in IEEE Xplore. Abstract—Deepfake-as-a-Service...
This paper addresses vulnerabilities in standard federated learning aggregation against gradient inversion attacks by evaluating three layered privacy-enhancing mechanisms: classical differential privacy (DP) noise injection, homomorphic encryption, and a novel quantum-inspired random unitary rotation of embedding vect...
The rapid evolution of sixth-generation (6G) wireless networks is increasing the demand for AI-enabled edge intel ligence that can operate across devices with different capabil ities, resource constraints, and availability. Existing distributed learning paradigms, including Federated Learning (FL) and Split Learning (S...
Source code, benchmark suite, and generated client files for the paper Schema-Augmented LLM Prompting for Converting ML Training Scripts to Federated Learning Clients (ACM TOSEM, resubmission 2026). v2.0 corrects FedAvg to sample-count weighting, partitions the IID simulation, moves generated-module import to preflight...
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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