Results 221 to 230 of about 14,919,864 (281)

[A digital droplet PCR detection technique based on filter faster R-CNN]. [PDF]

Nan Fang Yi Ke Da Xue Xue Bao
Zhang Y, Chen B, Li J, Liang Y, Zhang H, Wu W, Zhang Y.   +6 more
europepmc   +1 more source

Video‐based diagnostics supported by artificial intelligence as an opportunity to address the epilepsy diagnostic gap: A narrative review

Epilepsia, EarlyView.
Abstract Despite advancements in epilepsy care, a substantial diagnostic gap persists, particularly in resource‐limited settings. This narrative review explores the potential of video‐based diagnostics augmented by artificial intelligence (AI) to address this gap by enabling earlier and more accessible seizure detection and classification.
Gadi Miron, Robert Terziev, Maximilian Schöls, John McLaren, Tobias Loddenkemper, Jörg Wellmer, Sigrid Mues, Christian Meisel   +7 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

Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study

Epilepsia, EarlyView.
Abstract Objective Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative magnetic resonance imaging (MRI). Current methods for resection cavity masking are time‐consuming and labor‐intensive, and existing automated approaches exhibit variable segmentation ...
Jieun Seo, Mathilde Ripart, Helene Kaas, Cornelius Kronlage, Ben Sinclair, Lucy Vivash, Merran R. Courtney, Terence J. O'Brien, Siby Gopinath, Harilal Parasuram, Sedat Kandemirli, Natally Alarab, Lillian Lai, Marcus Likeman, Kai Zhang, Jiajie Mo, Georgian Ciobotaru, James Galea, Philip Sequeiros‐Peggs, Khalid Hamandi, Hua Xie, Venkata Sita Priyanka Illapani, William D. Gaillard, Nathan T. Cohen, Alexander G. Weil, Florence Henrichon‐Goulet, Kenza S. Lahlou, Aristides Hadjinicolaou, Agustín Ibáñez, Gonzalo M. Rojas‐Costa, Horst Urbach, Lara Bücheler, Marcel Heers, Adrián Valls Carbó, Rafael Toledano, Giulia Nobile, Costanza Parodi, Domenico Tortora, Alessandro Consales, Antonella Riva, Mariasavina Severino, Martin Tisdall, Felice D'Arco, Kshitij Mankad, Aswin Chari, Maria H. Eriksson, Rory J. Piper, J. Helen Cross, Torsten Baldeweg, Sofia González‐Ortiz, Jose Pariente, Nuria Bargalló, Yawu Liu, Reetta Kälviäinen, Carmen Barba, Matteo Lenge, Renzo Guerrini, Masaki Iwasaki, Daichi Sone, Hiroyuki Maki, Tomoki Imokawa, Noriko Sato, Julien Jung, Francisco Sepulveda, Daniel Mansilla, Andres Goycoolea, Ingeborg Lopez, Antonio Napolitano, Alessandro De Benedictis, Luca De Palma, Maria Camilla Rossi‐Espagnet, Nikolaos Kondylidis, Kostakis Gkiatis, Kyriakos Garganis, Joshua Pepper, Stefano Seri, John S. Duncan, Clarissa L. Yasuda, Lucas Scárdua‐Silva, Marina K. M. Alvim, Fernando Cendes, Antonio G. Gennari, Ruth O'Gorman Tuura, Georgia Ramantani, Mariam Josyula, Joel Stein, Nishant Sinha, Kate Davis, R. Edward Hogan, Luigi Maccotta, Sophie Adler, Konrad Wagstyl   +91 more
wiley   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
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

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

Variance‐Empirical Mode Decomposition Method for Fault Detection in MMC‐HVDC Transmission Lines

Energy Science &Engineering, EarlyView.
A variance‐embedded empirical mode decomposition (VEMD) method is proposed for fast and accurate fault detection in MMC‐HVDC transmission lines. By combining variance analysis with EMD, the method reliably detects various faults without communication links and remains robust to noise and non‐fault transients.
Seyed Amir Hosseini, Behrooz Taheri
wiley   +1 more source

A Probabilistic Fractional Order Physics Informed Mamba Kolmogorov‐Arnold Network for State‐of‐Charge Estimation in Grid‐Connected Battery Energy Storage Systems

Energy Science &Engineering, EarlyView.
This paper introduces the probabilistic fractional‐order Mam‐KAN (PFO‐Mam‐KAN) controller, a physics‐informed gray‐box framework for real‐time battery state‐of‐charge estimation. By unifying efficient Mamba encoders with uncertainty‐aware fractional physics, it achieves superior 0.31% RMSE accuracy and robust grid‐support operation under dynamic ...
Arun Kumar Rawat, Subhash Chandra, Vinay Kumar Deolia   +2 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