Results 101 to 110 of about 9,194 (254)

Generative Models in Inorganic Crystals Discovery and Inverse Design

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Generative inverse‐design samples from the vast inorganic crystal design space by starting from target properties such as band gap, stability, and ion transport. This Review examines the representations, generative models, and validation workflows needed to translate candidate structures into stable, potentially synthesizable materials for applications
Tao Li, Xiaolin Liu, Fei Wang, Yan Xiong, Yu Chen, Yan Zhao   +5 more
wiley   +1 more source

Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy

Epilepsia, EarlyView.
Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie, Arianna Damiani, Mahsa Khoshkhou, Luis A. Sanchez, Rachel J. Smith, Shruti H. Agashe, Jorge A. Gonzalez‐Martinez, Jennifer Hopp, Adam S. Charles, Sridevi V. Sarma, Joon‐Yi Kang   +10 more
wiley   +1 more source

Frontiers in EEG as a tool for the management of pediatric epilepsy: Past, present, and future

Epilepsia Open, EarlyView.
Abstract Electroencephalography (EEG) has evolved into an indispensable tool in pediatric epilepsy, fundamentally transforming the diagnosis, classification, and management of this condition. This review chronicles the historical journey of EEG from its groundbreaking inception to its current pivotal role in delineating distinct pediatric epilepsy ...
Hiroki Nariai
wiley   +1 more source

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source

Smart Parking Systems: A Review of Optimization Techniques and Technological Advances

Energy Science &Engineering, EarlyView.
This review highlights optimization techniques and recent technological advances in smart parking systems, emphasizing their role in enhancing efficiency, reducing congestion, and improving user experience. It provides insights into innovative approaches that shape the future of sustainable and intelligent urban mobility. ABSTRACT In heavily populated,
Amirhossein Khosravi Sarvenoee, Mohammad Kohzadipour, Majid Valizadeh, Mohammad Saeedi Kia, Milad Karimian, Mohammad Sharifirad   +5 more
wiley   +1 more source

Range-aware graph positional encoding via high-order pretraining: theory and practice

Machine Learning: Science and Technology
Unsupervised pre-training on vast amounts of graph data is critical in real-world applications wherein labeled data is limited, such as molecule properties prediction or materials science.
Viet Anh Nguyen, Nhat Khang Ngo, Truong-Son Hy   +2 more
doaj   +1 more source

Dissecting Spatiotemporal Structures in Spatial Transcriptomics via Diffusion-Based Adversarial Learning

Research
Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues.
Haiyun Wang, Jianping Zhao, Qing Nie, Chunhou Zheng, Xiaoqiang Sun   +4 more
doaj   +1 more source

Generalized Graph Transformer Variational Autoencoder

CoRR
Graph link prediction has long been a central problem in graph representation learning in both network analysis and generative modeling. Recent progress in deep learning has introduced increasingly sophisticated architectures for capturing relational dependencies within graph-structured data.
openaire   +2 more sources

Conformal load prediction with transductive graph autoencoders

Machine Learning
Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals.
Rui Luo 0002, Nicolò Colombo
openaire   +4 more sources