14 papers · ranked by Valyu relevance
Łukasz Ledziński, Grzegorz Grześk, Ludmiła Daniłowicz-Szymanowicz, Elżbieta Wabich
'Elżbieta Wabich'] As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital…
Shrouk H. Hessen, Hatem M. Abdul-kader, Ayman E. Khedr, Rashed K. Salem
'Rashed K. Salem'] Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an…
Steven A. Frank, Antonio M. Scarfone
Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure despite their apparent differences. Here, I show that a simple notational partitioning of change by the Price equation reveals a universal force-metric-bias (FMB) law: $Δθ=(Mf+b+ξ)$. The force $f$ drives…
Paola Patricia Ariza-Colpas, Enrico Vicario, Ana Isabel Oviedo-Carrascal, Shariq Butt Aziz + 7 more
The Assisted Living Environments Research Area-AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative…
Angeliki Pantazi, Bipin Rajendran, Osvaldo Simeone, Emre Neftci
The brain is equipped with impressive learning capabilities, enabling animals to dynamically adapt to the surrounding world. Hebbian learning and Spike-Timing Dependent Plasticity (STDP) are commonly employed learning rules in neuro-inspired models. The convergence properties and computational characteristics remain…
Hanzhong Zhang, Jibin Yin, Haoyang Wang, Michael E. Hahn
Based on Maslow’s hierarchy of needs theory, we have proposed a novel machine learning algorithm that combines factors of the environment and its own needs to make decisions for different states of an agent. This means it can be applied to the gait generation of a quadruped robot, which needs to make demand decisions.…
Jayashree Rajesh Prasad, Shashikant V. Athawale, Roshani Raut, Sonali Patil + 2 more
The current work describes a blockchain-based optimization approach that mimics the psychological mental illness evaluation procedure and evaluates mental fitness. Combining lightweight models with blockchains can give a variety of benefits in the healthcare business. This study aims to offer an improved review and…
William L. Tong, Anisha Iyer, Venkatesh N. Murthy, Gautam Reddy
Dogs and laboratory mice are commonly trained to perform complex tasks by guiding them through a curriculum of simpler tasks (‘shaping’). What are the principles behind effective shaping strategies? Here, we propose a machine learning framework for shaping animal behavior, where an autonomous teacher agent decides its…
Magdalena Kozielska, Franz J. Weissing, Xingru Chen
Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions…
Rachel St. Clair, L. Andrew Coward, Susan Schneider
Various interpretations of the literature detailing the neural basis of learning have in part led to disagreements concerning how consciousness arises. Further, artificial learning model design has suffered in replicating intelligence as it occurs in the human brain. Here, we present a novel learning model, which we…
Yu-Sheng Su, Yu-Da Lin, Tai-Quan Liu
To understand students’ learning behaviors, this study uses machine learning technologies to analyze the data of interactive learning environments, and then predicts students’ learning outcomes. This study adopted a variety of machine learning classification methods, quizzes, and programming system logs, found that…
S. Bianchi, I. Muñoz-Martin, E. Covi, A. Bricalli + 6 more
'A. Regev' 'G. Molas' 'J. F. Nodin' 'F. Andrieu' 'D. Ielmini'] Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is…
Younes Strittmatter, Stefano Sarao Mannelli, Miguel Ruiz-Garcia, Sebastian Musslick + 1 more
The sequencing of training trials can significantly influence learning outcomes in humans and neural networks. However, studies comparing the effects of training curricula between the two have typically focused on the acquisition of multiple tasks. Here, we investigate curriculum learning in a single perceptual…
Justin Zobel, Felisa J. Vázquez-Abad, Pauline Lin, Quanquan Gu
Machine learning is widely used for personalisation, that is, to tune systems with the aim of adapting their behaviour to the responses of humans. This tuning relies on quantified features that capture the human actions, and also on objective functions-that is, proxies - that are intended to represent desirable…