reinforcement learning

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...

Personalized Learning Pathways: A Review

In today&s;s rapidly evolving educational landscape, personalized learning recommendation systems (PLRS) have become essential for addressing diverse learner needs in an online environment. This review paper provides a comprehensive analysis of recent advancements in adaptive and personalized recommendation techniques...

Adaptive Cloud Workflow Scheduling via LSTM-Enhanced Deep Reinforcement Learning

Advanced Machine Learning Techniques for Crime Prediction in Smart Heterogeneous Policing Networks

The proposed chapter suggest a highly complex machine learning crime prediction model on an intelligent heterogeneous policing network, which will have to consider dynamic environment, multimodal, and real-time decision-making. A deep learning (CNN LSTM) hybrid architecture that is founded on graph neural networks and...

Adaptive optimization and interaction method for immersive exhibition space experiences integrating multimodal physiological sensing

Interactions in immersive exhibition halls rely heavily on explicit inputs such as controllers and touchscreens, making it difficult to respond in real time to users’ unconscious emotional fluctuations, which frequently disrupts immersion. This paper proposes an adaptive optimization and interaction method that integra...

Decomposed Q-Learning for Fixture Selection in Multi-resource-Constrained Flexible Job Shop Scheduling