13th Edition of International Conference on Neurology and Brain Disorders
October 19-21, 2026
Machine learning
Machine Learning is a branch of Artificial Intelligence that uses algorithms to construct models to automate decision-making processes. It involves analysis of data to identify patterns and behavior that are too complex for humans to detect. Machine Learning algorithms can be used to predict, classify, cluster, and optimize data. Machine Learning is based on the idea of learning from past experiences. This means that machines can observe patterns in data and learn from it. By presenting data to the model, the model is able to learn how to analyze the data and make decisions. This process works by recognizing patterns in the data and using these patterns to make predictions. The most common type of Machine Learning is supervised learning. In supervised learning, the data is labeled and the machine is trained to recognize the labels. This type of Machine Learning can be used for classification, regression, and time series modeling. Unsupervised learning does not require labeled data. This type of Machine Learning uses algorithms to group the data together based on similarities. Unsupervised learning is often used for clustering, feature engineering, and anomaly detection. Finally, Reinforcement Learning uses a feedback loop to teach a model how to behave and make decisions.
Ken Ware
NeuroPhysics Therapy Institute and Research Centre, Australia
Yong Xiao Wang
Albany Medical College, United States
Joe Sam Robinson
Mercer University, United States
Daniel Curry
Texas Children’s Hospital, United States
Steven Benvenisti
Davis, Saperstein Salomon, PC, United States
Andrzej Stanislaw Cieplak
Independent researcher, United States
Younok Dumortier Shin
OnusBio, United StatesSubmit your abstract Today
Title : Atypical presentation of Juvenile myoclonic epilepsy in a 16-year-old female: A case report
George Diaz, Memorial Healthcare Systems, United States
Title : Triple-network dysfunction, ME/CFS, and the NeuroPhysics Treatment Process “A dynamical systems perspective on psychophysical organization and environmental interaction”
Ken Ware, NeuroPhysics Therapy Institute and Research Centre, Australia
Title : Beyond plaques: 10-nanometer-scale pathoconnectomics of Alzheimer’s disease
Ons M Saad, Panluminate Inc., United States
Title : Results from the global phase 3 trial (IB1001-303) evaluating levacetylleucine in children and adults with ataxia-telangiectasia
Chidinma Ejim, IntraBio, United States
Title : A phase 3, multinational, randomized, placebo-controlled, double-blind, crossover trial (IB1001-304) evaluating levacetylleucine in CACNA1A
Chidinma Ejim, IntraBio, United States
Title : Clinical and molecular phenotype of PTRHD1-associated juvenile parkinsonism
Bhavan A Modi, Barrow Neurological Institute at Phoenix Children's, United States