Suchergebnisse - ML: Reinforcement Learning Algorithms

Artificial intelligence and machine learning applications in biopharmaceutical manufacturing von Rathore, Anurag S., Nikita, Saxena, Thakur, Garima, Mishra, Somesh

“… –ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning …”
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Emerging Technologies for 6G Communication Networks: Machine Learning Approaches von Puspitasari, Annisa Anggun, An, To Truong, Alsharif, Mohammed H., Lee, Byung Moo

“… Therefore, using the ML algorithm and its derivatives …”
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AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture von Masood, Fahad, Khan, Wajid Ullah, Jan, Sana Ullah, Ahmad, Jawad

“… As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL …”
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Systematic literature review on reinforcement learning in non-communicable disease interventions von Zhao, Yanfeng, Chaw, Jun Kit, Liu, Lin, Chaw, Sook Hui, Ang, Mei Choo, Ting, Tin Tin

“… ) algorithms have been successfully applied to preventing and controlling NCDs. Reinforcement learning (RL …”
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Reinforcement learning in robotic applications: a comprehensive survey von Singh, Bharat, Kumar, Rajesh, Singh, Vinay Pratap

“… ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain …”
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Machine Learning: Algorithms, Real-World Applications and Research Directions von Sarker, Iqbal H.

“… ), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area …”
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A scoping review of methodologies for applying artificial intelligence to physical activity interventions von An, Ruopeng, Shen, Jing, Wang, Junjie, Yang, Yuyi

“… •An increasing trend was observed of adopting state-of-the-art deep learning and reinforcement learning models over standard machine learning …”
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Using Knowledge Graphs and Reinforcement Learning for Malware Analysis von Piplai, Aritran, Ranade, Priyanka, Kotal, Anantaa, Mittal, Sudip, Narayanan, Sandeep Nair, Joshi, Anupam

“… Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points …”
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A machine learning tutorial for spatial auditory display using head-related transfer functions von McMullen, Kyla, Wan, Yunhao

“… This work addresses the use of ML techniques such as dimensionality reduction, unsupervised learning, supervised learning, reinforcement learning, and deep learning algorithms …”
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A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management von Boroujeni, Sayed Pedram Haeri, Razi, Abolfazl, Khoshdel, Sahand, Afghah, Fatemeh, Coen, Janice L., O’Neill, Leo, Fule, Peter, Watts, Adam, Kokolakis, Nick-Marios T., Vamvoudakis, Kyriakos G.

“… ) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management …”
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Navigating the evolution of two-dimensional carbon nitride research: integrating machine learning into conventional approaches von Mondal, Deep, Datta, Sujoy, Jana, Debnarayan

“… and structural properties. Recent advances in machine learning (ML) have opened new avenues for exploring and optimizing the potential of these materials …”
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Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems von Dang, Tran Khanh, Tran, Thanh Cong, Tuan, Luc Minh, Tiep, Mai Viet

“… In this work, we study and evaluate recent advances in machine learning (ML) algorithms and deep reinforcement learning (DRL …”
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Machine learning applications in prostate cancer magnetic resonance imaging von Cuocolo, Renato, Cipullo, Maria Brunella, Stanzione, Arnaldo, Ugga, Lorenzo, Romeo, Valeria, Radice, Leonardo, Brunetti, Arturo, Imbriaco, Massimo

“… With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML …”
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Optimizing portfolio selection through stock ranking and matching: A reinforcement learning approach von Alzaman, Chaher

“… This study presents a novel methodology that synergizes reinforcement learning (RL) with advanced ML algorithms …”
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Cost-Effective Autonomous Drone Navigation Using Reinforcement Learning: Simulation and Real-World Validation von Czarnecki, Tomasz, Stawowy, Marek, Kadłubowski, Adam

“… To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL …”
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Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques von Lin, Qiuyi, Li, Jin

“… ) algorithm and reinforcement learning (RL) for EM classification. The proposed model utilizes an ensemble of convolutional neural networks (CNNs …”
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Machine learning in gastrointestinal surgery von Sakamoto, Takashi, Goto, Tadahiro, Fujiogi, Michimasa, Kawarai Lefor, Alan

“… Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations …”
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A Novel FPGA-Based Circuit Simulator for Accelerating Reinforcement Learning-Based Design of Power Converters von Xu, Zhenyu, Yu, Miaoxiang, Cai, Jillian, Yang, Qing, Jeong, Yeonho, Wei, Tao

“… Recent research has shown that machine learning (ML) algorithms, such as reinforcement learning (RL …”
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A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications von Puspitasari, Annisa Anggun, Lee, Byung Moo

“… In addition, machine learning (ML) is widely used in many technologies because it can create machines that mimic human mindsets with mathematical algorithms without requiring direct human assistance …”
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Fully Automated Design Method Based on Reinforcement Learning and Surrogate Modelling for Antenna Array Decoupling von Wei, Zhaohui, Zhou, Zhao, Wang, Peng, Ren, Jian, Yin, Yingzeng, Pedersen, Gert Frolund, Shen, Ming

“… The proposed approach combines advanced reinforcement learning (RL) algorithms and deep neural networks (DNNs …”
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