reinforcement learning

Cost-effective tariff-aware HVAC coordination in Saudi residential buildings using physics-informed deep reinforcement learning

Abstract When multiple On/Off split air-conditioning units in Saudi residential buildings activate simultaneously, the resulting peak demand spike stresses the electrical grid and inflates monthly bills under the kingdom’s two-tier tariff (0.18 SAR/kWh $$\leq$$ 6,000 kWh; 0.30 SAR/kWh above). This paper proposes a Phys...

Deep Reinforcement Learning for Security-Aware Task Offloading in IoT Edge Computing: A Systematic Survey of Frameworks, Challenges, and Future Directions

Eight concrete research directions follow, from testbed validation and federated DRL to 5G channel modeling and grounding reward weights in real security-cost data.

A comprehensive survey of Edge-Fog-Cloud-IoT architectures: Intelligence, Security, and Sustainability

This survey provides an overview of Edge-Fog-Cloud-IoT architectural models and the technologies that have driven them during 2020–2026 and presents a holistic framework and a five-layer reference model that encompass the functional, performance, and trust aspects of distributed systems.

Calibrated Confidence Expression for Radiology Report Generation

ConRad (Confidence Calibration for Radiology Reports), a reinforcement learning framework for fine-tuning medical LVLMs to produce calibrated verbalized confidence estimates alongside radiology reports, is introduced and substantially improves calibration and outperforms competing methods.

Feedback-driven rule induction and retrieval-augmented bias mitigation for large language models

A feedback-driven external alignment memory framework for post-hoc gender bias mitigation is proposed that transforms identified bias into concise corrective rules through an iterative feedback process and is incorporated during inference to improve fairness in black-box large language models without retraining or modi...

Efficient Communication Management in Diffractive Optics IoT Networks for Intelligent Video Transmission

DOECOM-VID is suggested, a novel architecture that employs diffractive optical elements (DOEs) for interference-free parallel data distribution, phase-front shaping, and multi-channel optical beamforming for real-time video transmission in dynamic, intelligent video-centric Internet of Things networks.

Product and Distribution Strategies for Educational Services from the Perspective of Islamic Education Management in the Era of Digital Transformation: A Field Study at MIN 1 West Jakarta

Digitalization in Islamic education is becoming increasingly relevant for developing effective product and distribution strategies to address the challenges of the digital transformation era. This study aims to analyze the implementation of product and distribution strategies for educational services within the framewo...

Reinforcement learning-driven vehicular content caching

In-network caching can shorten the distances between consumers and contents. Therefore, we leverage in-network caching to enable vehicles to rapidly access vehicular contents such as road conditions, thereby giving vehicles sufficient time to correctly handle potential road hazards ahead. We propose a reinforcement lea...

Levels of Autonomy in Robot-Assisted Laparoscopic Surgery: Current Capabilities and Evolutionary Pathways

The review concludes with the main barriers to clinical translation, including the sim-to-real gap, deformable tissue modeling, and safety and regulatory requirements, which show how embodied intelligence may influence future autonomous surgery in the operating room.

Comparing for the best: reinforcement learning for source citation identification with large language models

A reinforcement learning framework for energy-aware semiconductor manufacturing operations

Semiconductor manufacturing is among the most energy-intensive industrial activities because its production processes are highly complex, equipment is used at high levels, and operations must run continuously. In many traditional manufacturing settings, control strategies mainly aim at productivity—such as throughput,...

Computational Models of Emotion: A Brief Survey

Abstract Computational models of emotion (CMEs) have emerged as powerful tools for enabling the development of emotion-inspired intelligent artificial systems and advancing affective science by formalizing theories and enabling systematic experimentation. This chapter provides an overview of CMEs, examining their devel...