Results 191 to 200 of about 303,008 (292)

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

Guava leaf disease detection using support vector machine (SVM)

Smart Agricultural Technology
Keshav Kumar Ray, Anshu Kumari, Sanjeev Kumar, Rajendra Machavaram, Imran Shekh, Shrikant M. Deshmukh, Prashant Tadge   +6 more
openaire   +1 more source

Machine Learning‐Driven Classification and Production Capacity Prediction of Tight Sandstone Reservoirs: A Case Study of the Taiyuan Formation, Ordos Basin

Energy Science &Engineering, EarlyView.
On the basis of core and log data, a Bayesian‐Optimized Random Forest model achieved 92.76% accuracy in classifying tight sandstone reservoirs. A gray relational analysis‐derived evaluation index shows > 80% consistency with actual gas zones. ABSTRACT Tight sandstone gas (TSG), an unconventional oil–gas resource, has heterogeneous reservoirs ...
Yin Yuan, Haijiao Fu, Detian Yan, Gang Wang, Shuguang Yang, Mengmeng Liu, Qingling Jiang, Xiaoming Wang, Wei Wang   +8 more
wiley   +1 more source

Robust Control Using a Matrix Converter to Enhance Wind Turbine Systems

Energy Science &Engineering, EarlyView.
This study uses a more efficient and effective solution to improve the operational performance of a wind turbine‐based power system. This system uses a doubly fed induction generator and relies on a matrix converter and fractional‐order proportional–integral controller.
Sihem Ghoudelbourk, Habib Benbouhenni, Adil Yahdou, Zakaria Mohamed Salem Elbarary, Nicu Bizon   +4 more
wiley   +1 more source

Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation. [PDF]

Genome Biol Evol
Amin MR, Arnab SP, Khan M, DeGiorgio M.
europepmc   +1 more source

AI‐Driven Optimization of a Hybrid PV–Wind–BESS Microgrid for a Rural Educational Institution in Developing Countries

Energy Science &Engineering, EarlyView.
An AI‐driven CNN–LSTM forecasting framework is integrated with HOMER Pro to optimally design a grid‐connected PV–wind–BESS microgrid for a rural school in Bangladesh, achieving 91.7% renewable penetration, low energy cost (0.0397 USD/kWh), and an 81.5% reduction in CO2 emissions. ABSTRACT Hybrid renewable microgrid planning in HOMER Pro often relies on
Robiul Khan, Md. Wajed Ali, Md. Feroz Ali, Md. Shafiul Alam, Mohammad Ali, Imil Hamda Imran   +5 more
wiley   +1 more source

Predicting future kidney function in type 2 diabetes mellitus using machine learning and baseline health information. [PDF]

Sci Rep
Unoki-Kubota H, Nakajima K, Shimizu Y, Tamano S, Kajio H, Bouchi R, Yamashita S, Fukazawa Y, Seki N, Matsumoto M, Uemura Y, Yoneoka D, Ueki K, Noda M, Kaburagi Y.   +14 more
europepmc   +1 more source

Real‐Time Data‐Driven Fault Diagnosis of Photovoltaic Arrays Using an Edge‐Server Machine‐Learning Framework

Energy Science &Engineering, EarlyView.
A real‐time, data‐driven framework detects and classifies photovoltaic array faults using edge sensing and server‐side machine learning. Ensemble tree models achieve near‐perfect accuracy with low latency, enabling practical, low‐cost deployment for reliable PV monitoring and intelligent maintenance.
Premkumar Manoharan, Sowmya Ravichandran, Deepak Kumar Sinha, Chandran Ramakrishnan, Omar Almomani   +4 more
wiley   +1 more source

Development and validation of an interpretable machine learning model for predicting medium-to-giant coronary aneurysms in Kawasaki disease. [PDF]

Front Immunol
Zhang J, Li J, Dong J, You T, Hou M, Liu Y, He Y, Liu P, Wang Y, Li L, Ma S, Lv H, Huang H.   +12 more
europepmc   +1 more source