reinforcement learning

Prediction and optimization of surface quality in direct laser tangential turning of RB-SiC through interpretable machine learning model and reinforcement learning-based genetic algorithm

Machine Learning in E-Commerce: Trends, Applications, and Future Challenges

E-commerce platforms continuously generate large amounts of data from customer searches, browsing activities, purchases, reviews, and delivery operations. Machine learning (ML) turns this data into useful predictions and decisions that improve product recommendations, pricing, fraud detection, inventory management, and...

Teacher Personality Development: An Integrative Psychological Perspective on Psychoanalysis, Behaviorism, and Humanistic Theory

Although teacher personality has received considerable attention in educational and psychological research, integrated explanations of its development remain limited. This study examines teacher personality development through psychoanalytic, behaviorist, and humanistic perspectives to clarify their contributions to te...

Dataset: Operational data of RL, RBS and Hybrid smart-irrigation controllers for lettuce, Dois Vizinhos, Brazil (Dec 2025 – Apr 2026)

This dataset contains the operational data generated by a cloud-based smart-irrigation system that ran three irrigation decision strategies in parallel for lettuce at a university experimental farm in Dois Vizinhos, Paraná, Brazil:a Reinforcement Learning controller (PPO);a Rule-Based System (RBS);a Hybrid RL+RBS contr...

THE EL-RAKHAWI THEORY OF ALGORITHMIC CHEMO-SILICON NEUROMORPHIC DYNAMICS From Electrical Simulation to Sovereign Molecular Computing

The El-Rakhawi Theory of Algorithmic Chemo-Silicon Neuromorphic Dynamics (DOI: 10.5281/zenodo.23243625, Revised Edition) transforms computation from binary electrical emulation into sovereign molecular embodiment. Built on five pillars: (1) **Algorithmic Neurochemistry** via Chemical Reinforcement Learning using dopami...

An agent-oriented approach to the organization and development of distributed intelligent systems

   Objectives. The work set out to justify the expediency and provide a conceptualization of an agent-oriented approach to building distributed intelligent systems that operate under conditions of incomplete initial data, high stochasticity, and non-stationarity of the external environment. Formal models of agents a...

A Comparative Analysis of Machine Learning Techniques for Power, Performance, and Area Optimization in Low-Power VLSI Design

The continuous scaling of semiconductor technology has significantly increased the complexity of Very Large-Scale Integration (VLSI) design. Modern integrated circuits are required to deliver high computational performance while operating within strict power and area constraints. Traditional optimization approaches bas...

Data-driven asynchronous optimal control for 2-D MJRSs with structural constraints via reinforcement learning

Impaired value estimation over space explains turn-induced freezing of gait in Parkinson's disease: A cortico-basal ganglia-inspired reinforcement learning model

Parkinson's disease (PD) gait is characterized by reduced walking speed, short shuffling steps, increased double stance time, and increased step time variability. The most debilitating symptom in PD is freezing of gait (FOG), a context-dependent locomotion arrest, mostly occurring while encountering turns, obstacles, a...

Dynamic Model Selection for Energy Load Forecasting: A Comparative Study of Bandit, Reinforcement Learning, and Contextual Strategies

PowerGridSim: Event-Driven Grid Simulation with Auto-Switching Decision System

PowerGridSim is an event-driven power grid simulation platform built on VeraGrid. It enables researchers to model real-world grid disturbances (load changes, generator trips, line faults), automatically generate protection and reconfiguration rules, and visualize grid state evolution through interactive dashboards. The...

RL-optimized CNN–BiLSTM: A multi-layer adaptive deep learning framework for heart disease diagnosis using IoT sensor data and reinforcement learning