reinforcement learning

Well-log depth alignment: a critical review and field-based comparison of classical and machine-learning methods

Abstract Accurate well-log depth matching is important because even small depth errors can affect fluid-contact interpretation, net-pay estimates, well-to-well correlation, and reservoir modelling. In practice, electric wireline logging (EWL) and logging-while-drilling (LWD) logs often differ in depth because of cable...

Graph neural network-based tail-risk pricing for cyber insurance against cyber-physical attacks in power systems

Abstract Cyber insurance transfers the residual cyber risk that remains after technical controls are installed, yet conventional pricing models rarely condition losses on grid topology, operating state, communication dependencies, and physical consequences. We develop a graph neural network (GNN) framework for pricing...

IoT-ML for Smart City Traffic Management and Dynamic Routing

Urban areas face severe traffic congestion, pollution, and economic losses from rising populations and vehicle density. Traditional fixed-timing controls fail under dynamic conditions. IoT-ML integration enables smart city dynamic routing. IoT sensors, GPS, and connected vehicles provide real-time traffic data. ML tech...

Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning

Knowledge graph-enhanced pre-trained language model for organizational resilience assessment and dynamic capability reconstruction path mining

Abstract Organizational resilience now shapes which firms survive prolonged uncertainty, yet most assessment instruments remain fragmented across indicators and keep structured domain knowledge apart from semantic understanding. This study develops a domain-specific framework in which knowledge graph reasoning and pre-...

Integrating longitudinal–lateral control for autonomous vehicles using enhanced deep reinforcement learning

This research provides a coordinated control framework for integrating lateral and longitudinal controls that improve tracking performance and overall robustness and could accurately predict lateral distance, speed, and yaw angle with an average absolute percentage error of less than 3%, demonstrating its effectiveness...

AI-Based Traffic Control Using Machine Learning and Computer Vision for Smart Cities

Urban development has caused a robust strain in road networks, which results in excessive time delays, unforeseeable congestion, and an increase in fuel consumption. A lot of signals continue to operate on fixed cycles and cannot react to the dynamism of the traffic. The paper describes a smart traffic-control system w...

Emerging Cybersecurity Challenges in Machine Learning-Enabled Smart Heterogeneous Networks

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...

Reinforcement Learning in the Realm of Embodied Emotion

This chapter explores the groundbreaking intersection of reinforcement learning and emotions, heralding a new frontier in human-computer interaction. This chapter initiates an exploration of emotion-driven reinforcement learning, revealing how emotions can be harnessed to enhance interactions between humans and machine...

Blockchain-Enabled Secure Communication for Intelligent Digital Evidence Management Systems

Most existing systems have sufficient data integrity but lack appropriate communication and decision-making methods that allow for timely responses to change. This paper discusses the use of a blockchain-based design as a method for overcoming these limitations by utilizing secure communication protocols and smart evid...

Adaptive artificial intelligence framework for skill level prediction and music training recommendation

The Adaptive Performance-Driven Training System (APDTS) effectively combines performance prediction and adaptive decision-making to deliver personalized training strategies, offering a robust and scalable solution for intelligent music learning environments.

ML-Driven IoT Cybersecurity for Personalized Smart Retail

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...