AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,544 papers
This study presents a dual-domain defense framework that combines Stackelberg game theory with Deep Reinforcement Learning that offers a robust, adaptable toolset for CPS defense by clarifying the trade-offs between temporal abstractions in nonlinear and dynamic environments.
The restaurant industry has a problem with the changing customer demand that it cannot match with its fixed operational capacity, leading to loss of revenue and inefficient use of resources. Traditional static pricing models are not capable of adjusting to real-time demand changes and other contextual factors, thus res...
This work introduces an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions, which are then further optimized by a genetic algorithm, enabling real-time and interactive routing at scale.
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Current offline reinforcement learning algorithms typically apply equal learning strength to samples of different quality during training, which can limit the effective utilization of high-quality trajectories. To solve this problem, we propose Reward Shaping based on Trajectory Quality (RSTQ). RSTQ increases the rewar...
In distribution network and microgrids, energy storage (ES) systems possess four-quadrant operational capabilities, making them inherently high-quality resources for reactive power (RP) regulation. However, existing research has primarily focused on optimizing the active power of ES to achieve economic objectives, whil...
As the world of smart, connected public safety networks expands in scale and sophistication, it is increasingly susceptible to attacks combining cyber and physical capabilities. The chapter suggests a novel framework for threat detection based on AI, which involves the fusion of multimodal intelligence, graph neural re...
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....
This study aims to analyze the structure and implementation of fiqh (Islamic jurisprudence) learning methods within the pedagogical tradition of Sheikh Abu H. Hasanoel Bashri and to contribute to the development of fiqh pedagogy in dayah (traditional Islamic boarding schools). The study employs a qualitative approach w...
Modern supply chains face abrupt supply disruptions, under which the stable lead time assumption behind conventional inventory control no longer holds. Most replenishment models, including existing deep reinforcement learning (DRL) formulations, act as feedback controllers. A disruption is treated as an unobservable ev...
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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